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    "# Explaining quantitative measures of fairness\n",
    "\n",
    "This hands-on article connects explainable AI methods with fairness measures and shows how modern explainability methods can enhance the usefulness of quantitative fairness metrics. By using [SHAP](http://github.com/shap/shap) (a popular explainable AI tool) we can decompose measures of fairness and allocate responsibility for any observed disparity among each of the model's input features. Explaining these quantitative fairness metrics can reduce the concerning tendency to rely on them as opaque standards of fairness, and instead promote their informed use as tools for understanding how model behavior differs between groups.\n",
    "\n",
    "Quantitative fairness metrics seek to bring mathematical precision to the definition of fairness in machine learning [[1](https://books.google.com/books/about/The_Ethical_Algorithm.html?id=QmmtDwAAQBAJ&source=kp_book_description)]. Definitions of fairness however are deeply rooted in human ethical principles, and so on value judgements that often depend critically on the context in which a machine learning model is being used. This practical dependence on value judgements manifests itself in the mathematics of quantitative fairness measures as a set of trade-offs between sometimes mutually incompatible definitions of fairness [[2](https://arxiv.org/abs/1609.05807)]. Since fairness relies on context-dependent value judgements it is dangerous to treat quantitative fairness metrics as opaque black-box measures of fairness, since doing so may obscure important value judgment choices.\n",
    "\n",
    "<!--This article covers:\n",
    "\n",
    "1. How SHAP can be used to explain various measures of model fairness.\n",
    "2. What SHAP fairness explanations look like in various simulated scenarios.\n",
    "3. How introducing a protected feature can help distiguish between label bias vs. feature bias. \n",
    "4. Things you can't learn from a SHAP fairness explanation.-->\n",
    "\n",
    "## How SHAP can be used to explain various measures of model fairness\n",
    "\n",
    "This article is not about how to choose the \"correct\" measure of model fairness, but rather about explaining whichever metric you have chosen. Which fairness metric is most appropriate depends on the specifics of your context, such as what laws apply, how the output of the machine learning model impacts people, and what value you place on various outcomes and hence tradeoffs. Here we will use the classic [demographic parity](https://fairmlbook.org/classification.html) metric, since it is simple and closely connected to the legal notion of disparate impact. The same analysis can also be applied to other metrics such as [decision theory cost](https://arxiv.org/abs/1808.00023), [equalized odds](https://arxiv.org/pdf/1803.02453.pdf), [equal opportunity](https://ttic.uchicago.edu/~nati/Publications/HardtPriceSrebro2016.pdf), or [equal quality of service](https://github.com/fairlearn/fairlearn/blob/master/TERMINOLOGY.md). Demographic parity states that the output of the machine learning model should be equal between two or more groups. The demographic parity difference is then a measure of how much disparity there is between model outcomes in two groups of samples.\n",
    "\n",
    "**Since SHAP decomposes the model output into feature attributions with the same units as the original model output, we can first decompose the model output among each of the input features using SHAP, and then compute the demographic parity difference (or any other fairness metric) for each input feature seperately using the SHAP value for that feature.** Because the SHAP values sum up to the model's output, the sum of the demographic parity differences of the SHAP values also sum up to the demographic parity difference of the whole model.\n",
    "\n",
    "<!--To  will not explain\n",
    "\n",
    "The danger of treating quantitative fairness metrics as opaque, black-box measures of fairness is strikingly similar to a related problem of treating machine learning models themselves as opaque, black-box predictors. While using a black-box is reasonable in many cases, important problems and assumptions can often be hidden (and hence ignored) when users don't understand the reasons behind a model's behavior \\cite{ribeiro2016should}. In response to this problem many explainable AI methods have been developed to help users understand the behavior of modern complex models \\cite{vstrumbelj2014explaining,ribeiro2016should,lundberg2017unified}. Here we explore how to apply explainable AI methods to quantitative fairness metrics.-->\n",
    "\n",
    "\n",
    "## What SHAP fairness explanations look like in various simulated scenarios\n",
    "\n",
    "To help us explore the potential usefulness of explaining quantitative fairness metrics we consider a simple simulated scenario based on credit underwriting. In our simulation there are four underlying factors that drive the risk of default for a loan: income stability, income amount, spending restraint, and consistency. These underlying factors are not observed, but they variously influence four different observable features: job history, reported income, credit inquiries, and late payments. Using this simulation we generate random samples and then train a non-linear [XGBoost](https://xgboost.ai/) classifier to predict the probability of default. The same process also works for any other model type supported by SHAP, just remember that explanations of more complicated models hide more of the model's details.\n",
    "\n",
    "By introducing sex-specific reporting errors into a fully specified simulation we can observe how the biases caused by these errors are captured by our chosen fairness metric. In our simulated case the true labels (will default on a loan) are statistically independent of sex (the sensitive class we use to check for fairness). So any disparity between men and women means one or both groups are being modeled incorrectly due to feature measurement errors, labeling errors, or model errors. If the true labels you are predicting (which might be different than the training labels you have access to) are not statistically independent of the sensitive feature you are considering, then even a perfect model with no errors would fail demographic parity. In these cases fairness explanations can help you determine which sources of demographic disparity are valid.\n",
    "\n",
    "\n",
    "<!--This article explores how we can use modern explainable AI tools to enhance traditional quantitative measures of model fairness. It is practical and hands-on, so feel free to follow along in the associated [notebook]. I assume you have a basic understanding of how people measure fairness for machine learning models. If you have never before considered fairness in the context of machine learning, then I recommend starting with a basic introduction such as XXX. I am not writing this Here I do not  beforeIt is not meant to be a definitite  One futher disclaimer is that as the author of SHAP (a popular explainable AI tool) I am very familar with the strengths and weaknesses of explainable AI tools, but I do not consider myself a fairness expert. So consider this a thought-provoking guide on how explainable AI tools can enhance quantitative measures of model fairness\n",
    "\n",
    "I consider myself fairly well informed about explainable AI, but I \n",
    "\n",
    "Questions about fairness and equal treatment naturally arise whenever the outputs of a machine learning model impact people. For sensitive use-cases such as credit underwriting or crime prediction there are even laws that govern certain aspects of fairness. While fairness issues are not new, the rising popularily of machine learning model  \n",
    "\n",
    "Legal fairness protections are even legally encorced for sensitive use-cases such as credit underwriting or crime prediction, but is also important in many other situations such as quality of service, or you might not initially to consider whenever you are using m Quantifying the fairness of a machine learning model has recently received considerable attention in the research community, and many quantitative fairness metrics have been proposed. In parallel to this work on fairness, explaining the outputs of a machine learning model has also received considerable research attention. %Explainability is intricately connected to fairness, since good explanations enable users to understand a model's behavior and so judge its fairness.\n",
    "\n",
    "Here we connect explainability methods with fairness measures and show how recent explainability methods can enhance the usefulness of quantitative fairness metrics by decomposing them among the model's input features. Explaining quantitative fairness metrics can reduce our tendency to rely on them as opaque standards of fairness, and instead promote their informed use as tools for understanding model behavior between groups.\n",
    "  \n",
    "This notebook explores how SHAP can be used to explain quantitative measures of fairness, and so enhance their usefulness. To do this we consider a simple simulated scenario based on credit underwriting. In the simulation below there are four underlying factors that drive the risk of default for a loan: income stability, income amount, spending restraint, and consistency. These underlying factors are not observed, but they influence four different observable features in various ways: job history, reported income, credit inquiries, and late payments. Using this simulation we generate random samples and then train a non-linear gradient boosting tree classifier to predict the probability of default.\n",
    "\n",
    "By introducing sex-specific reporting errors into the simulation we can observe how the biases caused by these errors are captured by fairness metrics. For this analysis we use the classic statistical parity metric, though the same analysis works with other metrics. Note that for a more detailed description of fairness metrics you can check out the [fairlearn package's documentation](https://github.com/fairlearn/fairlearn/blob/master/TERMINOLOGY.md#fairness-of-ai-systems).-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# here we define a function that we can call to execute our simulation under\n",
    "# a variety of different alternative scenarios\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import shap\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "\n",
    "def run_credit_experiment(\n",
    "    N,\n",
    "    job_history_sex_impact=0,\n",
    "    reported_income_sex_impact=0,\n",
    "    income_sex_impact=0,\n",
    "    late_payments_sex_impact=0,\n",
    "    default_rate_sex_impact=0,\n",
    "    include_brandx_purchase_score=False,\n",
    "    include_sex=False,\n",
    "):\n",
    "    np.random.seed(0)\n",
    "    sex = np.random.randint(0, 2, N) == 1  # randomly half men and half women\n",
    "\n",
    "    # four hypothetical causal factors influence customer quality\n",
    "    # they are all scaled to the same units between 0-1\n",
    "    income_stability = np.random.rand(N)\n",
    "    income_amount = np.random.rand(N)\n",
    "    if income_sex_impact > 0:\n",
    "        income_amount -= income_sex_impact / 90000 * sex * np.random.rand(N)\n",
    "        income_amount -= income_amount.min()\n",
    "        income_amount /= income_amount.max()\n",
    "    spending_restraint = np.random.rand(N)\n",
    "    consistency = np.random.rand(N)\n",
    "\n",
    "    # intuitively this product says that high customer quality comes from simultaneously\n",
    "    # being strong in all factors\n",
    "    customer_quality = income_stability * income_amount * spending_restraint * consistency\n",
    "\n",
    "    # job history is a random function of the underlying income stability feature\n",
    "    job_history = np.maximum(\n",
    "        10 * income_stability + 2 * np.random.rand(N) - job_history_sex_impact * sex * np.random.rand(N),\n",
    "        0,\n",
    "    )\n",
    "\n",
    "    # reported income is a random function of the underlying income amount feature\n",
    "    reported_income = np.maximum(\n",
    "        10000\n",
    "        + 90000 * income_amount\n",
    "        + np.random.randn(N) * 10000\n",
    "        - reported_income_sex_impact * sex * np.random.rand(N),\n",
    "        0,\n",
    "    )\n",
    "\n",
    "    # credit inquiries is a random function of the underlying spending restraint and income amount features\n",
    "    credit_inquiries = np.round(6 * np.maximum(-spending_restraint + income_amount, 0)) + np.round(\n",
    "        np.random.rand(N) > 0.1\n",
    "    )\n",
    "\n",
    "    # credit inquiries is a random function of the underlying consistency and income stability features\n",
    "    late_payments = np.maximum(\n",
    "        np.round(3 * np.maximum((1 - consistency) + 0.2 * (1 - income_stability), 0))\n",
    "        + np.round(np.random.rand(N) > 0.1)\n",
    "        - np.round(late_payments_sex_impact * sex * np.random.rand(N)),\n",
    "        0,\n",
    "    )\n",
    "\n",
    "    # bundle everything into a data frame and define the labels based on the default rate and customer quality\n",
    "    X = pd.DataFrame(\n",
    "        {\n",
    "            \"Job history\": job_history,\n",
    "            \"Reported income\": reported_income,\n",
    "            \"Credit inquiries\": credit_inquiries,\n",
    "            \"Late payments\": late_payments,\n",
    "        }\n",
    "    )\n",
    "    default_rate = 0.40 + sex * default_rate_sex_impact\n",
    "    y = customer_quality < np.percentile(customer_quality, default_rate * 100)\n",
    "\n",
    "    if include_brandx_purchase_score:\n",
    "        brandx_purchase_score = sex + 0.8 * np.random.randn(N)\n",
    "        X[\"Brand X purchase score\"] = brandx_purchase_score\n",
    "\n",
    "    if include_sex:\n",
    "        X[\"Sex\"] = sex + 0\n",
    "\n",
    "    # build model\n",
    "    import xgboost\n",
    "\n",
    "    model = xgboost.XGBClassifier(max_depth=1, n_estimators=500, subsample=0.5, learning_rate=0.05)\n",
    "    model.fit(X, y)\n",
    "\n",
    "    # build explanation\n",
    "    import shap\n",
    "\n",
    "    explainer = shap.TreeExplainer(model, shap.sample(X, 100))\n",
    "    shap_values = explainer.shap_values(X)\n",
    "\n",
    "    return shap_values, sex, X, explainer.expected_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--## Scenario A: No reporting errors\n",
    "\n",
    "As a baseline experiment we refrain from introducing any sex-specific reporting errors. This results in no significant statistical parity difference between the credit score of men and women:-->\n",
    "\n",
    "## Scenario A: No reporting errors\n",
    "\n",
    "Our first experiment is a simple baseline check where we refrain from introducing any sex-specific reporting errors. While we could use any model output to measure demographic parity, we use the continuous log-odds score from a binary XGBoost classifier. As expected, this baseline experiment results in no significant demographic parity difference between the credit scores of men and women. We can see this by plotting the difference between the average credit score for women and men as a bar plot and noting that zero is close to the margin of error (note that negative values mean women have a lower average predicted risk than men, and positive values mean that women have a higher average predicted risk than men):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 460.8x79.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 123,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 10000\n",
    "shap_values_A, sex_A, X_A, ev_A = run_credit_experiment(N)\n",
    "model_outputs_A = ev_A + shap_values_A.sum(1)\n",
    "glabel = \"Demographic parity difference\\nof model output for women vs. men\"\n",
    "xmin = -0.8\n",
    "xmax = 0.8\n",
    "shap.group_difference_plot(shap_values_A.sum(1), sex_A, xmin=xmin, xmax=xmax, xlabel=glabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use SHAP to decompose the model output among each of the model's input features and then compute the demographic parity difference on the component attributed to each feature. As noted above, because the SHAP values sum up to the model's output, the sum of the demographic parity differences of the SHAP values for each feature sum up to the demographic parity difference of the whole model. This means that the sum of the bars below equals the bar above (the demographic parity difference of our baseline scenario model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x273.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 499
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "slabel = \"Demographic parity difference\\nof SHAP values for women vs. men\"\n",
    "shap.group_difference_plot(shap_values_A, sex_A, X_A.columns, xmin=xmin, xmax=xmax, xlabel=slabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario B: An under-reporting bias for women's income\n",
    "\n",
    "In our baseline scenario we designed a simulation where sex had no impact on any of the features or labels used by the model. Here in scenario B we introduce an under-reporting bias for women's income into the simulation. The point here is not how realistic it would be for women's income to be under-reported in the real-world, but rather how we can identify that a sex-specific bias has been introduced and understand where it came from. By plotting the difference in average model output (default risk) between women and men we can see that the income under-reporting bias has created a significant demographic parity difference where women now have a higher risk of default than men:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 95%|=================== | 9542/10000 [00:11<00:00]       "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x79.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 123,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values_B, sex_B, X_B, ev_B = run_credit_experiment(N, reported_income_sex_impact=30000)\n",
    "model_outputs_B = ev_B + shap_values_B.sum(1)\n",
    "shap.group_difference_plot(shap_values_B.sum(1), sex_B, xmin=xmin, xmax=xmax, xlabel=glabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this were a real application, this demographic parity difference might trigger an in-depth analysis of the model to determine what might be causing the disparity. While this investigation is challenging given just a single demographic parity difference value, it is much easier given the per-feature demographic parity decomposition based on SHAP. Using SHAP we can see there is a significant bias coming from the reported income feature that is increasing the risk of women disproportionately to men. This allows us to quickly identify which feature has the reporting bias that is causing our model to violate demographic parity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x273.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 499
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.group_difference_plot(shap_values_B, sex_B, X_B.columns, xmin=xmin, xmax=xmax, xlabel=slabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note at this point how our assumptions can impact the interpretation of SHAP fairness explanations. In our simulated scenario we know that women actually have identical income profiles to men, so when we see that the reported income feature is biased lower for women than for men, we know that has come from a bias in the measurement errors in the reported income feature. The best way to address this problem would be figure out how to debias the measurement errors in the reported income feature. Doing so would create a more accurate model that also has less demographic disparity. However, if we instead assume that women actually are making less money than men (and it is not just a reporting error), then we can't just \"fix\" the reported income feature. Instead we have to carefully consider how best to account for real differences in default risk between two protected groups. It is impossible to determine which of these two situations is happening using just the SHAP fairness explanation, since in both cases the reported income feature will be responsible for an observed disparity between the predicted risks of men and women.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario C: An under-reporting bias for women's late payments\n",
    "\n",
    "To verify that SHAP demographic parity explanations can correctly detect disparities regardless of the direction of effect or source feature, we repeat our previous experiment but instead of an under-reporting bias for income, we introduce an under-reporting bias for women's late payment rates. This results in a significant demographic parity difference for the model's output where now women have a lower average default risk than men:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x79.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 123,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values_C, sex_C, X_C, ev_C = run_credit_experiment(N, late_payments_sex_impact=2)\n",
    "model_outputs_C = ev_C + shap_values_C.sum(1)\n",
    "shap.group_difference_plot(shap_values_C.sum(1), sex_C, xmin=xmin, xmax=xmax, xlabel=glabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And as we would hope, the SHAP explanations correctly highlight the late payments feature as the cause of the model's demographic parity difference, as well as the direction of the effect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x273.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 499
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.group_difference_plot(shap_values_C, sex_C, X_C.columns, xmin=xmin, xmax=xmax, xlabel=slabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario D: An under-reporting bias for women's default rates\n",
    "\n",
    "The experiments above focused on introducing reporting errors for specific input features. Next we consider what happens when we introduce reporting errors on the training labels through an under-reporting bias on women's default rates (which means defaults are less likely to be reported for women than men). Interestingly, for our simulated scenario this results in no significant demographic parity differences in the model's output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x79.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 123,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values_D, sex_D, X_D, ev_D = run_credit_experiment(N, default_rate_sex_impact=-0.1)  # 20% change\n",
    "model_outputs_D = ev_D + shap_values_D.sum(1)\n",
    "shap.group_difference_plot(shap_values_D.sum(1), sex_D, xmin=xmin, xmax=xmax, xlabel=glabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also see no evidence of any demographic parity differences in the SHAP explanations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x273.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 269,
       "width": 499
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.group_difference_plot(shap_values_D, sex_D, X_D.columns, xmin=xmin, xmax=xmax, xlabel=slabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario E: An under-reporting bias for women's default rates, take 2\n",
    "\n",
    "It may at first be surprising that no demographic parity differences were caused when we introduced an under-reporting bias on women's default rates. This is because none of the four features in our simulation are significantly correlated with sex, so none of them could be effectively used to model the bias we introduced into the training labels. If we now instead provide a new feature (brand X purchase score) to the model that is correlated with sex, then we see a demographic parity difference emerge as that feature is used by the model to capture the sex-specific bias in the training labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 98%|===================| 9794/10000 [00:11<00:00]       "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x79.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 123,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values_E, sex_E, X_E, ev_E = run_credit_experiment(\n",
    "    N, default_rate_sex_impact=-0.1, include_brandx_purchase_score=True\n",
    ")\n",
    "model_outputs_E = ev_E + shap_values_E.sum(1)\n",
    "shap.group_difference_plot(shap_values_E.sum(1), sex_E, xmin=xmin, xmax=xmax, xlabel=glabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we explain the demographic parity difference with SHAP we see that, as expected, the brand X purchase score feature drives the difference. In this case it is not because we have a bias in how we measure the brand X purchase score feature, but rather because we have a bias in our training label that gets captured by any input features that are sufficiently correlated with sex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x338.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 318,
       "width": 542
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.group_difference_plot(shap_values_E, sex_E, X_E.columns, xmin=xmin, xmax=xmax, xlabel=slabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenario F: Teasing apart multiple under-reporting biases\n",
    "\n",
    "When there is a single cause of reporting bias then both the classic demographic parity test on the model's output, and the SHAP explanation of the demographic parity test capture the same bias effect (though the SHAP explanation can often have more statistical significance since it isolates the feature causing the bias). But what happens when there are multiple causes of bias occurring in a dataset? In this experiment we introduce two such biases, an under-reporting of women's default rates, and an under-reporting of women's job history. These biases tend to offset each other in the global average and so a demographic parity test on the model's output shows no measurable disparity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|===================| 9996/10000 [00:11<00:00]       "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x79.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 123,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values_F, sex_F, X_F, ev_F = run_credit_experiment(\n",
    "    N,\n",
    "    default_rate_sex_impact=-0.1,\n",
    "    include_brandx_purchase_score=True,\n",
    "    job_history_sex_impact=2,\n",
    ")\n",
    "model_outputs_F = ev_F + shap_values_F.sum(1)\n",
    "shap.group_difference_plot(shap_values_F.sum(1), sex_F, xmin=xmin, xmax=xmax, xlabel=glabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if we look at the SHAP explanation of the demographic parity difference we clearly see both (counteracting) biases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJUi8ZekiSJEmSpF4y9JAkSZIkSb1k6CFJkiRJknrJ0EOSJEmSJPWSoYckSZIkSeolQw9JkiRJktRLhh6SJEmSJKmXDD0kSZIkSVIvGXpIkiRJkqReMvSQJEmSJEm9ZOghSZIkSZJ6ydBDkiRJkiT1kqGHJEmSJEnqJUMPSZIkSZLUS4YekiRJkiSplww9JEmSJElSLxl6SJIkSZKkXjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHktYkmOT1JjX6iQXJflikmOSbL/YddV4SX7W/cyOmce+hw1+3uujbpIkSZLUd4YeG6fNgG2BfYB/BL6d5D6LW6WlwaBAkiRJkjRg6LHxuBNwk+51c+ABwAe6bdsDH0yy5SLVTZIkSZKkJcfQY+NxRVVd1r1+VVVfrKrHMxN87AD85SLWTwusqv6lqlJVyxa7LpIkjZOEJItdjVltLPWUJC08Q4+N3z8Nfb7/otVCkiRJkqQlxtBj4/e9oc83n1YwyRZJnp/kC91AqFcl+XmSs5I8YMp+Z3bjZHy6W35wko8kuTDJlUm+m+S4JDee5fybdWNunNud/+okq5K8P8mjpuz30KFBXG+V5OZJTkryvSSXd+vvnKSAU7rdNhszAOynJxz/YUneleQnSX6f5NdJvpTkyCTXn+Wadkjy/5Ks7Pb9eZJ3JrnbtP3mYtr4JEl2G7qu+ye5QZIXJ/lmd09+k+TTSR4xh/PcOMnfJjkvyS+778VPk3w+yQuT3GrCflt3g+1+Pcml3Xfh/5KckmTPKed7RVfvH3bLd05yRjfo65Xdz/Xvk2wxtM+23X7/m+SKrp5nJLnNHK5v9yRv6va9rLs/307y6iRT/5uRJEmStHGz2fzGb7it5q8nFkruDHwEuO3Ipp2AJwFPSnJCVb146smS5wBvGDnvHsA/AAcm2beqLhyz37bAOcDeI5tuARwAHJDkdOAZVTVtENI9gDOAHafVcy6S3AB4O/DkkU03AO7XvQ5N8siqumDM/ncDPkMbVHZgp+54ByR50rrWcY62Ar4E3HNk/UOAByc5rKpOHbdjknvTukjtNLLpVt3rAcDuwGEj+/0J8FFat6pht+tehyY5sqrePK3iSR4NvAfYYmj1HYCXA/skeQztO/vJ7rgDWwAHdde3d1X9bMLxn09rDTX6u27P7nVYksdU1Vem1VOSJEnSxsmWHhu/3Yc+f3dcgSQ7AZ+lPTz+GHgG7QFyG+BPgH/pih6d5PAp59oDeC3wZeDBwHbAHYGTgD90289KrttpNsn1gPfSAo+ihSZ36fa/H3B2V/RpwKtnud4VtO/t4d317AjsB/yANsjrs7ty1zIz8Ovg9ZiRY51KCyhWA6+hhQbbdsf9K+Bi4O7Ae7prGL6mLbt6bwtcAbwQ2IUWAjwe+BlwWnfe9e2NwG7Ac7s6bEe71pW0cOoNXeh0HUl2p4U2OwG/BY4B7kz7XtwWeBxwOnDVyH47AB+nXetvu/Pu3C3vB3yHNsPQm5I8dkq9twHeAXydFtBsRws8Tu62P4IWtpxFCy0O6uq6E/A82s9tJyZ8Z7rv8mu7fT/YnWOH7rU/8M2uDmd3/41IkiRJ6puq8rVEX8CxtJCggJ0nlPlAt/1q4FYTypzVlfkpsN2EMq/oyvwSuOHItjOH6vFvwA3G7P+ioTIHjGz786FtLxqzb2ihSNHCkzuMbH/o0P6XA3tMuWeHdeVWz3JvHzV0zD+fUObOwJVdmSeMbDtmaP9Hjdl3B+AXQ2WOmcfPf+K10EKOwbGvBvYaU+YeQ2UOH7P9vG7bpcCdp9Rj2cjyWwb1Au43pvzNgB92Zc4HNpvwXStaC5XrjznGl7rt1wAXjftuAyd0ZX4PbDmybbvuu1LAWyZc15a0oLCAN67Lf6vdS5I2OUO/z5fUa/ny5WPXS5IWxXp7Zp7Ly5YeG48bJdmye22fZJ8k76P9Nf5a4Gk1pol/9xfsx3eLz6+qiyYc/3jaA/72wMOm1OPoqrpqzPrXAD/pPh8ysu3p3fuPunLXUVUFPIf2EB3g0CnnP7mq/nfK9rl6Tvd+TlW9e1yBqvoWMNj2lJHNB3fvn6qqj47Z90LaPd0Q3lFV/z6mDv8FfKtb3Gt4W5K7AA/sFo/trnWsGupulGRzWosLgDOr6stjyv8aGHSTug0ttJrk6Kq6esz6s7r3ZcDrx323gXd17zcA7jqy7RnAjYBLaK1C1lBVlzHTSuTJoy2U1taqVatYtWqV613vetdvUus3Zkvxfrre9a53fR/XL7a0500tRUmOBV42S7EfA4+uqkldW54MvJP2F47bAL+ZcqwvAncDjquqY4eOcSZtOtxLgW2q6g8TzvX/gL8BLqqq7bt11+v22xJ4bVW9YNLJk5xHexD/UlXdf2j9Q4FPdYuPqKpPTjnGYbTBTK+tCVO9JlnW1elGtBYqb5l0POBZwInA+VW1c7f/9rQWMQB/UxPGregGAP1pt/j3VfWKKedZq2tJshutSw/Ak6vqXaP7d+XeTxsz5eyq2m9o/XOB13eL208Jw0aPtxettQ/AflV19oRy1wd+B1wfeGVVHTO07RXAS2ndgm4y7vvUdYv5cLd4n6r62pgyN6F1r4HWEuf9Q9s+ATyc1gVpNLAadifgq93nXapq5ZSys/GXqaRNzjrmxevN8uXLOeKII9ZY7797JWlRLOr/LBzIdOO3C3BCkidW1TVjtg/G/AgzD+Cz2X7C+u9PCjw6gxYY2yW5cVVdThszYctu/XdmOe+3aaHHtBk5fjzLMebiVrTAA9ogl/80pezA8D3ZeejzxFYnVfWzJJcxc/3ryy+mbLuie99iZP2u3fuquQYeneGBcCf+PKvq6m52lj2Z/PP85ZTv05VDn9cYRHZMmdHrG3zvH0sLX+Zie9o4KJKktbQ+woScNG1c89mcep06LdVwRpK0/tm9ZeOxS1WlqkIbPPMRwKBLw37AcRP222oe57rBhPWXz7LfZUOftxx5H90+zuDhdNrgn1dO2TZX63pPhqfmne2ezLZ9IVw7hzKj/9ob3OO5BgIDC/nznEu951pu9PoW8nsvSZIkaSNl6LERqqpLui4eD2ZmxpYXJbn9mOKDh+6LB6HJHF6HjTkOXPdhf5xxD8TjgpDZ9l/bB/G1NRxEPHaO92TZhP1nuyezbV8scwmYxlmKP89xBj+j16zF9/6Li1BPSZIkSeuRocdGrBuIcdBhdTPGj//xo+592yS3XsdT3mF06tYRe3TvF3VdW6ANJDl46L3jLMe/U/d+/jzrN1c/p814Am2Gk7W1cujzHpMKdWN6rO+uLfP1w+59pyTbrcV+K4c+T/x5dmN67NYtru+f5ziD7/18fr6SJEmSesLQYyNXVV8APt4t/kWSXUeKfIaZARYPWcfTbQXsO25Dks1o4ycA/HFGj27MhsHy4yaFJkl2BAaDl67LX9wH45pM/G5X1ZVD5zioq/ucVdWvmAkNHjel6LRti+0zQ58PmlhqTd9gprXHE6aUeywz3UUWowXFYODbP02yyyKcX5I2CYPpAJe6jaWekqSFZ+jRD4PxPDYDjh7eUFXnA4NZLY5Ocp9pB0qySzct6SQnJBk39sELmBmwcsXItlO7912B5485Z4A30AbWLeDt0+o4i4uHDrvjlHKv697vwCwDmSa5YZLRwThP694fluRRY/bZAXjJHOq7KKrq28B53eKxSaa12vhj155usNwzusWDkuw9pvzWwKu6xfOBTy9EndfSybRBXJcBpyWZ2s1oQtcwSZIkSRs5Q48eqKqvAoNpXJ/WdasYdiRtBowbAZ9N8uokeyXZNsl2Se6S5NAkH6JNgzrpAfHntO4Cn0myb7f/7kn+iZmH3C8AHxzZ773MPGD/U5LXJtkzyTbdQ/MHgAO77a+rqu+v9U2Y8V/MtGw5NsmtkmyeZNlwK5Oq+ggzD+9/m+RTSfZLcsskWyfZOcmjk7yB9uD++JHzvJ6Z2XDek+QFSW6bZPskj6O1brgBM1OqLkXPorXa2Ar4cpKju5/L1kluneQxSd7GzNS2A8cBv6IFCp9I8uwkt+mu/TG078AgRHhOVc11wNIFU1UXAs/pFh8A/GeSZyTZtbu+nZL8aZJjknybNi2xJEmSpJ5xytr+OA54OHB94CjgeYMNVbUqyQNpYcQdgRd1r3FWA5OmEf1fWquI1wOfnbD9wBppP1pVf0jyROAcYG9aa481WnwApwN/N+Hcc1JVP0/yflrXiyOYGfMEWpeOhw4tP4PWGuCIbv3wtlFXjZznsiSP7Y65LXBS9xou/0TgrcBN53Ux61lVfS/Jw2jfix1owdWrxhR928h+FyZ5JO3nuQPwxu417FrgyKo6e8ErPkdVdWrXdemNtCls/2VK8W9tmFpJkiRJ2pBs6dETVfVlZroRPDPJ9iPbvw/cDTiU9rD6C9pgnr+nDU75YVoIsGNVTWydUFX/DPwZbRyRi2gP998D/hG4V1VdMGG/i4F9gMNpgckltPE3LqA9dD+6qg6uqtVre+1jPBV4OfBNWqgx6VquqapnAfcCTumu4zJa8HMRbSySlwN3q6q3jNn/G8CdgTcBP6Hdz18AZwH37VqTLGldK6E70LrifBX4Ne06fgJ8jhag/cOY/f6TNojrcbTWNb+jfRd+TAsX7lpVb94AlzBVVZ1C61Z1PPCftOu7ltYC5xvAW2hh11MWq46SJEmS1p84qJNmk+RM4C+Bz1TVtNYQ0qbMX6aStIBy0vz/DrL8pqdy+OGHL2BtJEnrIIt5clt6SJIkSZKkXjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD82qqg6qqjhziyRJkiRpY2LoIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHpIkSZIkqZcMPSRJkiRJUi8ZekiSJEmSpF4y9JAkSZIkSb1k6CFJkiRJknrJ0EOSJEmSJPWSoYckSZIkSeolQw9JkiRJktRLhh6SJEmSJKmXDD0kSZIkSVIvGXpIkiRJkqReMvSQJEmSJEm9ZOghSZIkSZJ6ydBDkiRJkiT1kqGHJEmSJEnqJUMPSZIkSZLUS4YekiRJkiSpl5YtdgUkSZKkUXXU/P+ZevLJC1gRSdJGzZYekiRJkiSplww9JEmSJElSLxl6SJIkSZKkXjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHpIkSZIkqZcMPSRJkiRJUi8ZekiSJEmSpF4y9JAkSZIkSb1k6CFJkiRJknrJ0EOSJEmSJPWSoYckSZIkSeolQw9JkiRJktRLhh6SJEmSJKmXDD0kSZIkSVIvGXpIkiSpd3LSanLS6sWuhiRpkRl6SJIkSZKkXjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHpIkSZIkqZcMPSRJkiRJUi8ZekiSJEmSpF4y9JAkSZIkSb1k6CFJkiRJknrJ0EOSJEmSJPWSoYckSZIkSeolQw9JkiRJktRLhh6SJEmSJKmXDD0kSZIkSVIvGXpIkiRJkqReMvSQJEmSJEm9ZOghSZIkSZJ6ydBDkiRJkiT1kqGHJEmSJEnqJUMPSZIkSZLUS4YekiRJkiSplww9JEmSJElSLxl6SJIkSZKkXjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD11Hkp2THJPk80l+luSqJL9L8sMk707y1CQ3Wux6zibJvkmqe+08Zvtg2yHrsQ47D51n3/V1nsU0231ei+P0/l5JkiRJ2vAMPQRAks2TvAb4HvCPwAOAWwLXB7YEdgUOBE4Hzk/y9MWq6/qWZEX38H3eYtdFkiRJkjR/hh6ia7nxceBvaSHHD4HnA3cHbk4LP+4DvAxYCWwHHLkYdZUkSZIkaa6WLXYFtCS8CXhw93k58JyqumakzCrga0lOAI4CHr8B67fgqiob4BwrgfV+nsVUVeexANe4KdwrSdK6Sdr/JqqqF+eRJG0Yhh6buCQPAQ7pFs+uqmdNK19VVwPHJ/ng+q6bJEmSJEnrwu4temH3/gfWoggPi2UAACAASURBVMtKVX1neDnJIYOBKLvlXZK8Ocn/Jfl9kt+MHiPJFkme1w2aelGSq5OsSvKeJA+crQ5JHpzknCQXJ7kiyXeSHJdkyznsu8ZApoNrAA7uVj1wqNzgdexsxx463tTBOZOsHD5mkgOTfC7Jr7vr+a8kRybZbJbz3C7J25P8vLvX5yc5Jcmuk6512n0YU+bYrszKMdvmPGBskmXdz/vfumusJI+by70aOt5OSV6d5BtJLk1yZTfI7luS3G7KftdL8rQkn0xyYZJrujp8P8lHkjwnybaT9pckSZK0cbKlxyasCwce2i2e23UxWIjj7gOcA2w1tPr3I2X2BD4C7DKy+y2AJwJPTHJiVb1owjmOBl41svqOwD90+//9vC9gESR5CzDayubuwBuAvYG/nLDfg4GzgeEZdW4DHAY8KckjFr6283JD4LPA/ed7gCRPBE7jutcKbZDdXYFDkhxUVe8b2W8Z8CHgUSP7bd29bg88GvgpYAsmSZIkqUds6bFpuy8waEXwhQU87ruBS4Cn0AZB3Ql42mBjkh1pD8C7AOcDz6Q9tG4D3IM2rgjAC5P89ejBkzyamcDjW7QH1psDu9FCj92A18yj3mcCNwHe0S1/sVsefh0/j+PO5mnAEcA/A3cDtgXuCXy62/6U7pqvI8lOwPtpIcAl3TFuxcz9vhJ413qo73wcQxsM93jgTrTBcO9L+/nNKslDad+rG9G+q/vRrnM74CHAebRg5Z1J7jGy+6HMBB5vAu5NC9cGA/QeAXye1tpJkiRJUo/Y0mPTNtzK4n8X8LibA/eqqguG1n146PNraSHFKuDeVfXLoW2/Bp6V5ALabDH/mGRFVV0xVOak7v1HwAOqatB15ldd+R/RAoy1UlWrgcuSrO5WXVtVl63tceZhF+CFVXXS0LpLkuwHfJ8WZBxMaz0z7B9orWlWAw+vqv8c2nZGkq8BX19/1V4rtwSeXlVvH1p38Vx27FpqvI0W0n4CeFRVDQcU5yb5HPBJ2oC8xwOPHNo++PyBqnr2yOFXAV8DTp7rhUiSFlcy+7jXy5cvhxdu3sq/cJbCkqRes6XHpu1mQ58vXcDj/tNI4PFHSXYAntQtvmAk8Bh2AnAZrfXHH7toJNkb2KNbPG4o8PijqnoH8G/zrPtiOJ8WBF1HVV0JvLdb3Gt4WxcEPKVbPGMk8Bjs/31ay4al4Fsjgcfa2J/WZadowckaLTKq6lpaSAbwiCTbDG0etGZaNc/zz8mqVatYtWrNU7je9a53vesXdv1iWWr3wfWud73rN5b1iy1Ox7XpGhkX4xFV9cl1ONYhwOCh9k6jA50OlXsScBbtAXYXpv+1/zxaN49XVtUx3f7PpwUEBWwzLvToyg1f2y6j45V0A5YCHFpVK0a2raC1rPhcVe07pX5TdQN7/rhbfFA3vevw9pXAbYG3VdVhE47x17Tg4sqqutHQ+nsw04pj/6r68IT97wN8pVscd60T78NQmWNpgcL5VbXzyLZ9aV2VYPp9njg+S1duZybcqyRvAv4a+A5tfJNJtgAGIdrDqurT3f7H0VrFXAEcDrynm4VoofnLVJLWo7m08BhYvnw5RxxxxDqdz38jS9KCmfsv8PXAlh6btkuGPm+9gMf98ZRtu3fvAVYCv5vyumdXdvuh/Xfu3i+YFHh0FrK7zvr2iynbBt16thhZv/PQ52nXulTuw7TvxGwG35k9mf59GW41NPydeR1tkNIb0bo9XZQ268/RSe6dtflXtCRp0VXVrC8ATrwGTrxmTuXX2FeS1BuGHpu24QfRPSaWWktdt4xJtpqybZIbDH2+cfd++Sz7bIixOBbKtfPY58ZDn6fdi6VyH6Z9J2azTt+ZLhy7N/AWWjeum9AGNn0VbTyPHyZ5yriDSJIkSdq4OZDppu0rtAfuzYAHbKBzDh7QL62q+bQuGex/46mlYMt5HHtjMhx0TLsXC3EfFvv3xOBaP1RVj5vPAboxZv46yZG0FkT3pc368nDgdsA7kmxdVW9eiApLkiRJWhps6bEJ62YmGUyL+uBuXIX17Ufd+1ZJdplacryV3fuOSaa1AFiwlitL1Mqhz9Oudbb78PvufbT7zLBbzKVC69HgOzM6Fe1aq6rVVfW1qnp9VT2WFngMugD9g11dJEmSpH4x9NBgmtTrAf88152S7DnP850LDGbfOGQe+395UAXarB6TzKtFQOea7n2zqaUW1zdp41jA9Gud7T4MZtm5/biNSa5HaxGxmD7Vvd8myYMW8sBV9XNgebe4Aws7to0kaQFtqDE3HNtDkvrF0GMT181wcUa3+Ngkb02y+aTySa6f5CXA6fM838+A93SLL0pyv2nlk9w2yfD4DF9j5i/zL0uyxkNqNz7DtFk+ZjOYUWaxWzhMVFWrgXd2iwcluedomSR3AJ49y6H+vXt/wvB9HvJc2gwzi+l9tIFIAZZ30x5PlGT3keXZWrvs2r1fzUyQJEmSJKkHDD0E8FfA57rPRwDfSfLcJHdNsl2SWyTZO8nfA98DXsm6jfPwPGAVcEPg3CQndrNobNe97pzkkCQfBH5IG3hy2FHd++2Azyd5ZLff7ZIcQ5s6d+U61G8wFeyuSY5Isn2SZd1rKf0383LawJybA59M8swkOyXZMclBtCl/fzXLMU7r3m8DfCjJPZLcLMldkrwOeA0z3UsWRTe97CG08WduD/xXkucluWOSrbvr3TvJ85N8FXjvyCE+luQr3fa9k+zQfV/unuTVwN905d7XhUmSJEmSemKxByjUElBVlyd5OPBPwF8DuwGvn7LLhbSH4fme74Ik+wIfAO5ECzGOmlD8WkZmN6mqc5K8mDb7xl2Aj47s813gGFoLgfk4mxaa7Ay8tXsNHAccO8/jLqiqWpXk8bT6bgOcPFLkt8DjaQPWTjrGOUneCTwFeET3GvYm4CLgZQtV7/moqnOT7Ae8g9YC53VTin99ZDnAfbrXJP8JHLlOlZQkSZK05Cylv1prEVXV1VX1PGB32gPuF4Ff0Jr8X05rcfFu4CBg56o6Y9Kx5ni+HwB3Bw6mPbQPzvV7WuBwNvBMYMeq+vWY/U+gjTXxMeDXtClRvwccT+vacsk61O1K2mw2p9BaOVw132Otb1V1LnBXYAWt9czVtK4gbwfuVVVfncNhnkZ74P9v2n28FPg8cGBVzdY9ZoOpqo8CuwAvpn0/L6YFYpfRgq7TaWOY7DOy65/RWhd9mNY16lJgNS28+xTte3afqrpo/V+FJEmSpA0pDtQk9VuSwX/kh1bVisWsS8/5y1SSloiTTz6ZI377dADqKBs2S9IiW9QZEm3pIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHpIkSZIkqZcczlrquapa1NGSJUmSJGmx2NJDkiRJkiT1kqGHJEmSJEnqJUMPSZIkSZLUS4YekiRJkiSplww9JEmSJElSLxl6SJIkSZKkXjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHpIkSZIkqZcMPSRJkiRJUi8ZekiSJEmSpF4y9JAkSZIkSb1k6CFJkiRJknrJ0EOSJEmSJPWSoYckSZIkSeolQw9JkiRJktRLhh6SJEmSJKmXDD0kSZIkSVIvLVvsCkiSJEkLrY7yn7mSJFt6SJIkSZKknjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHpIkSZIkqZcMPSRJkiRJUi8ZekiSJEmSpF4y9JAkSZIkSb1k6CFJkiRJknrJ0EOSJEmSJPWSoYckSZIkSeolQw9JkiRJktRLhh6SJEmSJKmXDD0kSZIkSVIvGXpIkiRJkqReMvSQJEmSJEm9tGyxKyBJkiTNRU5aPadyy2+6nisiSdpo2NJDkiRJkiT1kqGHJEmSJEnqJUMPSZIkSZLUS4YekiRJkiSplww9JEmSJElSLxl6SJIkSZKkXjL0kCRJkiRJvWToIUmSJEmSesnQQ5IkSZIk9ZKhhyRJkiRJ6iVDD0mSJEmS1EuGHpIkSZIkqZcMPSRJkiRJUi8ZekiSJEmSpF4y9JAkSZIkSb1k6CFJkiRJknrJ0EOSJEmSJPWSoYckSZIkSeolQw9JkiRJktRLhh6SJEmSJKmXDD0kSZIkSVIvGXpIkiRJkqReMvSQJEmSJEm9ZOghSZIkSZJ6ydBDkiRJkiT1kqGHJEmSJEnqJUMPSZIkSZLUS4YekiRJkiSplww9JEmSJElSLxl6aA1Jjk1S3Wvnxa6PJEmSJEnzYeihJSPJzkNhy76LXR8tfUlWdt+XYxe7LpIkSZKWHkMPSZIkSZLUS4YekiRJ2iCSkKT355QkLR2GHpIkSZIkqZcMPbSgktwyybOSfCTJT5NcleSyJN9N8qYku03YbyXw46FVnx0a36OS1IT9bp/k/3XHvyzJ5Um+neTEJDusw3Wc1513Rbd8QJJzk1yU5Iok30hyVJLNpxxjtyR/m+RTSX6R5Ookl3b7vjrJLSbs993u3G+fQz1/MK7s0H07JMn1kvxNkv9I8tvuGj6e5L4j+zwsyUeTXJDkyiT/neSZc6jDZkmenuQTSS7srvPC7jtwwJT9BgPmruyWd0/y9iQ/6743P01ySpJbjdl3RfeduG236mWj35fRcWGS7JfkQ0l+3tXxt0l+2P18XpTk1rNdqyRJkqSNy7LFroB651vA1iPrrg/s0b0OTfIXVfXhdT1RkiOB17Dm93jP7vWMJPtV1RfX8TwnAH83svquwInA45M8vKouG9lnK+AHYw63ebfvXbv6PaaqvjpS5u3Aq4EnJnl2VV0+oV77AIMQacWE6m8OfBR4xMj6RwAPSrJ/VX08yd8DLx8pczfg5CS7VNVLJtThlsDZwD1GNt0ceDTw6CTvBA6pqmsm1JEkDwU+AGw5tPpWwGHdMe5bVedP2n82SZYDh4+s3hy4CbAr8FDgauD18z2HJEmSpKXHlh5aaD+kPbA/jBY8bAfcAXgS8FVgC+DMMX+93xO409Dyo2gPpMOvP0rydOANtMDjw7SH1h1oD9v7Ad8AbgZ8aFxLgbXwQFrgcQ5w3+567g68rdt+X+CUCfv+D/AyYF9gd2Bb4I7AocB3uuX3JLnxyH6nA6tpAcATptTt4O79x8DnJ5R5SXf+lwK37+r/WOBntDBqeZIn0gKPtwP37Op1L2AQFv1dkjsxoqv3p2iBx8XA82nB1ja0n+fxwLXAU4BXTrmOrYGzgO8Cj6T9DG/b1f0PwC2Ak0b2OYL2nfhJt/wq1vy+fKGr58OYCTzeBTwAuCWwI7AX8DTgY8DEUEaSJEnSxsmWHlpQVbXXmNUXAz9I8gHgs7SHzmcBxwztd0WSK4b2uXK09cRAkm1ogQfAKVU1+hf8s5OcC/wb7eH7pcBfzed6gJ1pgcd+VfWHoes5rKvvc4C/SPLaqvr3oeu5lNZSYtQlwP8meQ/w37SWGk8G/mVo3wuSfIwWThxMC0GuI8kNgQO7xdOqamz3n67+B1TVB4fWfaSr+2eA2wD/Cry2ql4wXM8k+9MClZsCTwWOHjn2S2khzmXAPlX1vaFtvwZemuQHtDDl+Un+uap+NqaOWwH/AfxpVf1+aP2rkmwLvADYP8lW3X2lqq4Crhrq9nT1pO8LLUgB+HpVPXlk24Xduc+YsK8kaT3I+h5YdPlyBy+VJAG29NAGVFXX0v7SDvCQdTjUobRWEJcCR0441+W0v/5DCyXW5V8+fzsUeAx7Ke2BH+CQtTlgV78PdIvj7sWp3fuDktxmzPbH0cKCAk6bcqrPjwQeg/OfC/yqW/w9rUXKaJlLaC05AO49vC3JMlpwBfDKkcBj+BgraK1/ltFa+0xy9EjgMTAIIzantbCZj82691/Mc/85WbVqFatWrXK9613vetdPWb+ULMX743rXu971fVy/2DL5D8TaVCU5lpmH4F2qauVa7r8PbSyG+9K6EdwYGA0dLqmqbUf225mZwUwfVFXnTTj+ObTuLx9jprXDOLvT/ooPcPuq+uFaXMN5tK4t362qPaeU+yCwP/CNqlrjoTzJo2jdJ/aidae40ZjDfL2q7jmy3zJaF5QdgL+vqleMbP8Y8GfAZ6vqwWPOO/gP+5iqGtu1JMnXaGHGp6vqYRPKvBp4ESP3IcletJY00Fru/Pe4/TunAY8H3lFVBw0d41ja9+wqYMuqWj3m/DcCBmOa/HlVnTWyfSWtK8xxVXXshGs4lBYi/YHWBedtk8ZJWUf+MpWkWWyo1hfLly/niCOOuM46/80rSYtmUZve2b1FCyrJa2kPlrPZah1Os3v3/kjgd3PcZ3tai4O19b9z2L4/M7OIAH8MLd7B9FBmYI17UVWrk5xJ69pxMPDH0KOb9WUQUqyY5dgXTNl25VqU2WJk/e5Dn78wSx0Gtp+w/lfjAg/4Y7enweJoHebqTODZwJ/QukW9OsmXaeOgnAd8sWuFJEnaQOYbQOSksf+7GOPUP57Dbi6StGmze4sWTJKDmAk8Pkt74L8jbfDMweCSg7E1NlvjAHM3n8DkBvM812wtAgbdW7YcWX80M4HH+2jByG60QUIH9+KEbvuk8HHQxWW3JPcbWn8Q7f5d1h17mrk8zM+lzOi/GBfyZzDXwGFe/2rtZo15EO1+/xK4IfBg4Fha6PGzJEeuYxcoSZIkSUuQLT20kAZjPHwReOi4cTC6ATjX1eW0IOUNVfW8BTjeNKMzq4wahB2jg2gO2tT+a1U9ZdyOSaa2XKiq73RdUPamjRny5W7T07r3s9ZTN425GD7vzarqN4tUjzmpqt8CL07yEtp0wfejBSGPonU7egNtitwXLVolJUmSJC04W3poId21e3/vhIE/Ae68AOf5Ufd+jwU41mz2mOP28wcrutllBtPkvnvKvnO5F4PWHgcmuWGSew7tt2IO+68vPxr6vCF+Dguimm9U1Vuq6kDg1sxM9/u8JDddxOpJkiRJWmCGHlpIg+4LY7uudINSPm7K/tcMfZ7W/WUwo8g+SXabe/Xm5Y5J7jBuQ5ItmZl55UtDm4a7cUy6F7ekDZQ6m3cBV9C6kxxAG98D4P+qaq5jaawPX2VmPJVDFrEeg+/MvLpLVdWvgdd1i5sDuy5EpSRJ41XVBh9QdDHOKUlaOgw9tJAGM688ZsL2k2hjWkzya2ZmwLjFlHKn0LpXbAac1oUPE00KLdbCa5KM+2/leGa6t6wYWv8rZrp/PHZMfTYD3socupd13TIG43Y8E3hy93naNLXrXVVdDby5W3xqkidOK5/k5kluth6qcnH3PvH7kmT3Sds6w0HHxRNLSZIkSdroGHpoNvdIcp9ZXoOH2fd07w9KckaSuyfZNsm9k7ybNojpdyedqKquYGa2lGcnuUuSLZIs62ZDGZS7CPjrbvF+wNeTHJZktyRbJ9kpyQOSvDTJt4DXrsP1n08LcT6UZO8k23T1OgV4TlfmXVX170P1Ww18oFs8JMlJSe7Y3Yt9gU90x5x4L0YMurg8iDaWSbHIoUfnH4Fv0gYYfXeSk5P8aRdwbJNkjyR/keQdwErWTyuKr3fv+yd5SJKbDr4vQwOTLk/yzSQv6b4Xt+jqt2eSFwODKX2/UlU/WQ91lCRJkrRIHMhUs3n/HMocAHwQeDWtZcPdaDOMHDTmWOcAb5tyrH8G3kIbvPN/Rrb9cXaNqjq9C0LeBNye1vpjku/NfgkTnQdcSBvgclwLlq8Ah49Z/3e07iu3pk07+4KR7W8AfgO8bA51+Bzwf8yEBucuhYfzqro8ycNoYdcDaC1RnjmpONftvrRQ3gocRguDPj2y7UG0nx+0cVBeyWQ/Ap660JWTJEmStLhs6aEFU1WX0R5+T6A9pF8DXEKbzeUZwBOBSQOcDo7xVtrsJF+ghQITy1fVqbQg4JXAf9C6x1wL/JYWmLwVeBjw5+twWVTV39Gmn/1cd44ru+O/EHhgVf1uzD6rgL1oXUB+SrsXvwQ+CTx+bWadqdYRecXQqhXjS254VXUhLdw5gBZ+/BS4Crga+Dntep8L3KaqvrEezv8/wL7Ah4ALgNVjih1Mm03nPcC3ad/J1bSuLJ+nBVJ3qar/W+j6SZIkSVpccWAnaU1JzqM9zJ9WVYcsbm0gyVHAibTBQ3fsugJpafGXqSStZzlpXLa9puU3PfX/s3fv8ZbN9R/HX28zQu6MS65D5Ba5/BCqGXRRIhUSlUFJREK6kEYXcgtFhdIkJIUopUKjSFEKuZXLyLXcmXHn8/vj+932mj177b3POXuffc467+fjsR777LW+67u+67vWXmevz/6u75c992zWENPMzPpA7ZP0jlt6mI0OU/LrTxzwMDMzMzMz64yDHmYjnKTNgbXy21Z9l5iZmZmZmVmBOzI1G4HysLbjgLWpDw37x4i4pn+lMjMzMzMzG10c9DAbmS4j9SlS8xzQceenZmZmZmZm5sdbzEa6p0ijxmwZEdf1uzBmZmZmZmajiVt6mDUREZPH8vbNzMzMzMyqwC09zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySxve7AGZmZmZmnYiDOvvqeuqpPS6ImZmNGm7pYWZmZmZmZmaV5KCHmZmZmZmZmVWSgx5mZmZmZmZmVkkOepiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZmZmZmZmVWSgx5mZmZmZmZmVkkOepiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZmZmZmZmVWSgx5mZmZmZmZmVkkOepiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZmZmZmZmVWSgx5mZmZmZmZmVkkOepiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZmZmZmZmVXS+H4XwMzMzKzqdOyL/S7CmHLKQv0ugZmZjRRu6WFmZmZmZmZmleSgh5mZmZmZmZlVkoMeZmZmZmZmZlZJDnqYmZmZmZmZWSU56GFmZmZmZmZmleSgh5mZmZmZmZlVkoMeZmZmZmZmZlZJDnqYmZmZmZmZWSU56GFmZmZmZmZmleSgh5mZmZmZmZlVkoMeZmZmZmZmZlZJDnqYmZmZmZmZWSU56GFmZmZmZmZmleSgh5mZmZmZmZlVkoMeZmZmZmZmZlZJDnqYmZmZmZmZWSU56GFmZmZmZmZmleSgh5mZmZmZmZlVkoMeZmZmZmZmZlZJDnqYmZmZmZmZWSU56GFmZmZmZmZmleSgh5mZmZmZmZlVkoMeZmZmZmZmZlZJDnqYmZmZmZmZWSU56GFmZmZmZmZmleSgh5mZmZmZmZlVkoMeZmZmZmZmZlZJDnqYmZmZmZmZWSUNOeghaaqkKJmelXS3pHMlvb0bBR4tJE0u1MPEAa67dWHdgzpI/5uc9lFJSw22zFUmaXquo2n9LouZmZmZmZkNj1639JgHWAHYAfiNpFMkqcfbHPUi4mLg3Pz2cEkrlaWV9CGgFlA6OCL+2+vymZmZmZmZmY0G3Q56rAUsWJhWAXYEbs3L9wT26fI2q2o/4HHg1cB3myWQNAE4Pr+9Avj+8BTNzMzMzMzMbOTrdtDj6YiYWZjuiIifApsDT+Q0+3V5m5WUW2wcnN++PbfoaPQNYALwHPDxiIjhKp+ZmZmZmZnZSDcsHZlGxIPAr/PbVSUtNBzbrYDvAX/If39D0uK1BZLeBnw4v/1aRNw23IUzMzOz5iTR9Inez8ydJjMzMxsWwzl6y38Kf8/XuLCxo0lJ75F0iaQHJb0k6YRC2gmSdpX0M0l35Q5Tn5Z0h6QfSFqvrBCSJhY6CZ0saR5Jn5V0g6RZkh6XdJmkd7bbIUlbSLpY0iN5+zdLOlzSAgOrmuZyy42Pk1pyLEFq2YGk4iMvNwFHDXYbTer9vZIul/Rw3qfrJR0kqek3NElTavXZZjszcrqpHZSh9NgX1lld0rck3STpiXzsbpP0U0k7SJqnTXneJunXkh7K588tkg6TNMe5WVhnFUkHSPqdpAckPZ+3fb2koyS9ps02V85lvjmX91lJ90r6m6QTJW3eYt1FJH1R0jW5w9rnlDoJPkPSuq22246kdSWdLunf+Zg/I+k/kv4s6euS/q/Fusvlfb9O0mN53dsl/TKfGwuWrLeKpG9L+lfe5lP5M3iE0mNbZdsbzLmyqqST8jGemev+JknHyB3/mpmZmZlV2vhh3Nby+fV54H+tEko6Bmg1asnvgGY3eivn6cOSPhkRTfvCKFiQ1JJio4b5WwBbSPp4RJxaUsbPAUc2zF4DOAzYHvhim213JCJulXQkMBX4iKQzgK1I+xnAnhHxfDe2JenrwGcbZq8DHAO8T9LbI2JmN7bVogztjj2SDgEOB8Y1LHpdnrYnPVI1vWT9zwFHAMWf4FbPeW4pacuIeLFhnYWBfzfJbm5SHa0D7CHp3RHx5ybb3BL4BXMG/JbN0/rAJJqc15LeDJxPepSpaAVSa59dJO0fEd9qtr+tSPow8APmrMvl87QxqW62a7LubsB3SB0WF702T1vn99OabPP7pLorWjtPe0naNiKubFP2Ts6V/YDjmPNat2ae9uhkW2ZmZmZmNjoNS0uP/GvqVvnt5W36nngr6UbmZ8CmpBu9NaiPZgJwH3AS8C7STdISpCDA1sAlpBu4kySt36ZoJ5Ju6D6d15+Q87wzLz9e0pJN9mdr6gGPf+btLknquPWw/Hpcm20PxJHALfnvOguEMwAAIABJREFUH+byAnw3Iv7UpW1MIgU8LgY2IdXFutQ7R90EOK1L2yrT9thLOhj4KukY/wP4AOnmf3HSubA3MEfQoWASKeDxY1Kwa3FSB7xn5+VvIbWuaeYG4EvAZGC1vO4awG7Azfn9TyXNX1xJ0lykwMJ8wO3ALqRzZHHSjfdWpPN5jmCgpLWA3+S6uBH4YGF/NwHOI32Ov5nPy45JWpQUtBgH/JUU2Fgpb2tt4D2kgMUTTdb9AHA6KeBxJ7AH6TO0WK6b3YDfkgJzxfUm5TznznWxA7B03qe9gEeBRYGLJa3YovidnCu7kz7j44GL8jpLkT6r2wLX521dKGm5FtsyMzMzM7NRSkPt+1LpcYUv5bdrMftjLEuQfsH+MunmbiaweUT8tUk+00k3pABnRMSuQyjT2aSbw7Mi4kMNyyYCd+W3LwJvbvxlXtLapBtcgH0i4tsNy28hBUvuBDaIiMcblu8CnFmYtVJEzBjs/uQ830RqlVJrnXA/sEZEPDnEfKdTr/eLgW0j4uWGNN8E9s1vN4qIawvLppBu6ImI0uGIJc0AVgQOj4ipLcpQeuzzsfs36Sb2d8A2EfFcSdrxxdYaDds4OSI+2ZBewDXA/wHXRkRj65+WcqDjH6Rgxsci4nuFZeuQbrAB3hARNzTJoizfq0g39TcAb4yIZ5qkmQbsSgqMrdVph7aStgUuBF4CloyIRztcb0HgblLA4HpgcuNnoJC28TjcQAqo3Ev67PyvIf26pKDVPMBPImKnhuXT6excWSyXcQHgtIjYs0ma+UnHfE1SAPETbXa9HXckbGavUO7Po3ZJ1rH5Uljrz+OYF/pRrDHjlIVOZ88957j0m5lZf5TeJw6Hbrf0uAl4qjDdSfoldg3gHFKAYY6AR4MXqY9aMli1gMOWbdKd0+xRhIi4kXQDC7BhcZmkWnN/SDfwc9zsRcRZpJupbroamFF4/9WhBjyaOKAx4JEdQgpYAUzp8jaL2h37T5ACHi8Au5UFPAAaH08pmAV8oUn6oH7erKuSPkxabG8WcEF+23jeFR8dub/TPCVtQAp4QBqdZ46AR3Zofl2D5o99lamVaxZpeOROfYgU8IAU4CldtyHgsSEp4AHpszNHy5aI+AdwSn77fkmLlGTd7lzZjRTweIKSEaPyMau12NpJatbjoJnZ0KjWoak7MDUzM+uL4erIVKRHAj4g6VVt0v49D9faOkNpHUknK3Ui+aSkl1XvUPPinGzpso4Us9+0WFbrv6Gxo8PaTWiQmsyXuaDFssHYn/ToQc3u+bGJbrklIv7VbEFEPAVclt9u1sVtNmp37LfIr5dHxH2D3MafWwSLasd8buo39bOR9C5J5yh1mjtL9U5xA/hMTva6htVuA57Nf/9A0ms7LGttf58Ebpa0QLOJFLB4KKfdoMO8IbUeCWAh4DS16Yi1SbluK7b66cCbCn+f3yLdT/PreOCNJWk6PVf+BIxvUXe1x8YWIfVDMmj3338/998/Z0zL8z3f8z3f+muknQ+e7/me7/ljbX6/dfvxltke45A0ntRJ4zbAV0g3Fn8C3tr4q3Wh2fq5EfGBNts8ADiaOTtfbGb5iLi3sO5E6o+3vDUiLmu2UuGRgekRsXlh/omkX44fiIhlWpRxO+qBjyE93pL7NrgJmB+4lXpLk09FxDcHm2/Oezqp3i+IiPe1SFfr5PTxiFi0MH8K3Xu8peWxl1Tr7+HoiGjscLWlwjbOjIgPl6SZDPw+v50YEXcXlo0HzgJ27GBzd0TEKg15HwgcW5h1E/BH4ArgdxHxSJPyfI/UV8ZAfCEiGjvYLSXpW0DtUZ+Xgb8XynVps45rJV0HrEcHn9WG9U4APgU8GBGlAZbc10jtUZvZOhMewLlyOwMPYmwaEVcPcJ0iP95iZq9o23jMj7f0lB9vMTMbUSr1eMtsIuLFiLg7Ik4iBRAgtZT4XIvVyprwA6/0bXEcKeDx95zv60n9hyyYp2KHjq1GqHmp5Q7kTTa8r3VSOavNet0c5eQ7ebuPkVrMXJLnf1XSsl3aRqf705XheEu0PPakYwvp0anB6uSYw5zH/XPUAx7nkTr5rHVGWjvvvp6Xz3HORcRxpE47a493rUXquPPHwAOSzpS0dMNqC3dY1qKWQ/U2sV8uxy2k68EGpFZFFwD/Uxpit7G11GCPQ+3caffZKOZb1lKr3bkyHHVnZtZWRKR+PY55wYEOMzOzPhiux1uIiIuoj06xU6u0beyVX+8k/TJ7RkTcFBEPR8TM/Mt0u0dohqIWHJi/ZaouBQckfRB4Z357cG7S/wngadIN4YCHKS3R6f403rB2+ut2N4ZHrt0Mt3pkqVdqI7r8OCK2j4iLIuKOiHi0cN41Dkc7m4j4WURsCLyGNKzuiaTzeG7SiC5XNQQYaufa9RGhDqepA9mpSE6JiDVJj0/tQupT4/68P58EftfwKNVgj0OngbPi8sEGuGp1d+IA6m76ILdlZmZmZmYj1LAFPbLa4wITh5DHOvn1ooh4tiTN64eQfzsz8uvSklr9mrx6i2UdySNQnJDf/pE8fGx+VGZqnv/ePArHULUrb2353Q3zXzkGkpre9OdHQyYMvmivuD2/rtMyVZfl41Ab0vQnLZJ2dN5FxIMRcV5E7E9qLXJAXrQyqZPQmtrQyauV1W03RcSMiDg7IvYiPYp0fF60MfCOQtLBHocZ+XXp/AhLmbUKfzeeb52q1d16g1zfzMzMzMwqYLiDHrWOOIfy6EetCXrT/jzyCAxDaUnSzp9qmyI94lBmuy5s61hgSeB5Ut8GxVYVx1MfBvWk3CnjUKwhqbEDTgBy3rURSa5qWPxg4e9VS/J+C915dKDW/8qWkkr7U+mBYtnLzrtlqQ+l2rHc0uJ40igjMHvw6Xf5dV56e043K9eLpKGma4rlqh2H1SX93wCyvbLwd2n/MaRWMJBGaPnLAPIvqtXdZpJWaZnSzMzMzMwqa9iCHpJ2pP5r/2BvZKDeCenbS0aCOZjZfynuqoj4C6kzUYAvNRtSU9LOpF/HB03S5qRhNwGOjIhbisvzTemepM4nlyd1FDtUx5WMCHME9UcOpjUs+wdpCFlIj0bMRtI81IcFHarvkm6E5wa+32okoNy6pFseov64xDZNtjUul63pNiUtK6n08SFJS1J/VOSVDk1zp5q1z8rRZUGpQj6rtVreJP1KbUZTKnYEWuxo9SxS/zIAp7Zq8VQ8Dnm46hvz2y9JmqP1j6R1SI9vAZzXajjcNk4jHbNxwA/bBQXb1a2Z2UC90pdHI/ftYWZmNqy6HfR4dcOQkAtLWkPSZ8mPZpD6gDh6CNuoDWe5GnChpI0lTagNYUvqTPKW8tW74qD8ujLwB0nvzGVYWdKhpNFMZgw2c0nzkvpVgDTcadOgQURcA5yc3+4raf3BbpP0GMG7qdfpYpLWlnQasG9Oc07jEKUR8QRwYX57oKTPSlou18c7SKOArEK9JcOg5dFUDslvtwKulrRD3t6iktaU9DFJVzL78KhD3e6L1EfimSLp2HxeL55HfPkNqe7Kzru3AfdKOkXSdpJeK2kRSStKej9wKemz+CKpk9SiPUgtoyYA10j6oqQ35OOzpKT1Je0l6VJgIMPHQuoE+G5JxysNxbtiLtfKknalPqzsU8CvC/Uxk3rfOusBf5U0RdLEvP6qknaR9CvmDITtSz1Qd5Wk9+b9WE7Sx4DLSS1rnqR1h8ctRcTDwN757abAdZI+KmmVXMZlJL1Z0iGS/gl8Y7DbMjMzMzOzkaubv4ZDGoazleeAfYfYYeA00igY7yDd+G7VsPxPpJYJvxzCNlqKiIslfZ4UjFgb+FVDkluAQ5nzBrZTXyQ9KhLAnhHxXIu0hwDvJfU5caqkjSOi0xFKiqYD/yW1lHl3k+VXk1qWNHMQsBmpg86vUx/FBNIN83bA6QxuRI3ZRMTRuX+Lw4D1gXOHmmeHPkt6fGV54MA8FZ0IPE59+OZGi5Dqr6wOXwT2jojZPkMRcZOkLUnn0nKkR06+3GR9qA/zOhBLk0Zr2b9k+dPAzhHxUEO5zpW0ECnotgp52OImZjs+EXGFpN2A7wGvox5YKXoM2HYowzznbZ2RW5qcTPo8ndYi+W1D2ZaZmZmZmY1MvX685SXSjeC1wFHAmhHR6sajrXxDvw3weeBmUiDlCdJQoAeShnRtN/zqkEXE10n9XPyadJP2DOnG6QjSoy2DuQFF0uuBz+S3p0fEH9qU4ynqLTE2II22MSgR8VnSsKxXUN+nG3J5JuVtNVvvbmAj0k3lvaTHXe4DfghsEBGXD7ZMJds7HFgXOJXUqeYzpODKraSb7O2p973SrW3eD2wIfBu4h7SP/wN+C7wvd0pa5lzSOftN4BrqdTSLdA5/G1in7LORW/S8jnScL83bfYEUkLg9578zA+8g+ATS8T4VuA54gBR8eYr02NIxwOoR0TSAGBHfI7W4OoEU8JxZKNMvSC1JftZkvTNIj6B9l/rxm0V69OVI4HURcWXjeoMREaeTHtP5Guka8RjpuvQk6dz+Lqklzge6sT0zMzMzMxtZ1PR5UxszJE0ntWD4YURM6W9pzEY1X0zNrJSOfbHfRRhTTlnodPbcs6xxpZmZDTP1c+PDPXqLmZmZmZmZmdmwcNDDzMzMzMzMzCrJQQ8zMzMzMzMzqyQHPczMzMzMzMyskhz0MDMzMzMzM7NKGt/vAlh/RcTkfpfBzMzMzMzMrBfc0sPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMzMzMzs0py0MPMzMzMzMzMKml8vwtgZmZmVnVxkL9yDadTT+13CczMbKRwSw8zMzMzMzMzqyQHPczMzMzMzMyskhz0MDMzMzMzM7NKctDDzMzMzMzMzCrJQQ8zMzMzMzMzqyQHPczMzMzMzMyskhz0MDMzMzMzM7NKctDDzMzMzMzMzCrJQQ8zMzMzMzMzqyQHPczMzMzMzMyskhz0MDMzMzMzM7NKctDDzMzMzMzMzCrJQQ8zMzMzMzMzqyQHPczMzMzMzMyskhz0MDMzMzMzM7NKctDDzMzMzMzMzCrJQQ8zMzMzMzMzqyQHPczMzMzMzMyskhz0MDMzMzMzM7NKctDDzMzMzMzMzCrJQQ8zMzMzMzMzqyQHPczMzMzMzMyskhQR/S6DmdmoJums9ddff+d+l8PMzJKHH36YCRMm9LsYZmYGXHfddWdHxC792r6DHmZmQyTpEWBe4NZ+l6XCVs+vruPecj33nuu491zHvec6Hh6u595zHffe6sCzEbF4vwowvl8bNjOrkBkAEbFBn8tRWZL+Bq7jXnM9957ruPdcx73nOh4erufecx33Xq2O+8l9epiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZmZmZmZmVWSgx5mZmZmZmZmVkkOepiZmZmZmZlZJXnIWjMzMzMzMzOrJLf0MDMzMzMzM7NKctDDzMzMzMzMzCrJQQ8zMzMzMzMzqyQHPczMzMzMzMyskhz0MDMzMzMzM7NKctDDzMzMzMzMzCrJQQ8zswGStJqkMyXdL+k5SXdL+o6k1wwhz50kXSrpYUkvSHpU0nRJu0kac9fqXtRxzndjSWdJuifn+7CkayQd1a2yjxa9quNC/ktLekRSSJrZjTxHm27WsaQVJO0l6QJJt0p6WtJTkq6TdJikhXqxDyOBpJ0l/VHSE5JmSvqrpH0Ge22UtJWk3+br7NOS/inpEEnzdLvso0U36ljSXJI2lfTVnNe9kp6X9F9Jv5K0XS/3YTTo9rnckPee+Xobkk7qRnlHox5cL8ZJ+rikP+T/ac/m7xC/kLRNt8s/GnSzjiUtKukISTdKmlX4X/kjSet2rcwR0a28zMwqT9Ik4NfAfMB1wL+BNwCrAw8Bb4qIfw0wz2nArsDLwFXA/cAywGak4PT5wPYxRi7YvajjnO9hwFQggGuBu4DFgTWBpSNifDfKPxr0qo4btnEhsA0gYFZELDCkQo8y3a5jSVeSrgkvAn8H7gQWAzYGFgJmAFtExF3d24v+k3QysDfwLHAZ8AKwJbAgcAGwQ0S8NID8DgaOAl4CpgOPAZOAJYA/A1tGxNNd3IURr1t1LGkV0nkO8CjwV1L9rgxsmOdPA3YfK//Pirp9LjfkvSJwI7AA6Zp7ckR8shvlHk16cL1YjHQd3wh4gvQd7SlgeWA94OyI+Gg392Gk62YdS1oB+COwAvAw8Jec77rAa0n/73aKiPOGXPCI8OTJkydPHUzA/MADpJvmTzYsOzbP/xs5oNxhnm/P6z0OvKFh2Xqkf7IBvLff+z9a6zivu1de99/AWg3LBLyx3/s+2uu4IZ+P5HxOyq8z+73fo72OgZ8A+wOLN8xfAvh9zvOKfu97l+vx/Xm/HgBWLcxfCrg5L/vUAPL7P1JweRawcWH+AsAVOb/j+73fo7WOSTcplwFbAeMalk0CZub8duv3fo/mem6St4BLc/1Oq117+73Po72OST86XZXXOw2Yv2H5AsDr+73fo7yOz87rXAy8uqHup+ZlDwNzD7ns/a48T548eRotE/DJfAH+fZNl44Db8/J3DSDPI/M63y1ZfmpefnS/938U1/HipF9mngNW7/c+9nvqRR035LEM6Vfev+SboLEY9OhpHTfJc7mcXwDL93v/u1iPf8379JEmyyYVvnzP1WF+P8vrHNZk2cqk1h/PAYv0e99Hax232dahOb/L+r3fVapn4BN5/X0LN4pjMejR7evFx/M60xnCjwBVmnpQx7UfB+b44Sn/r3w6L19zqGUfc8+Jm5kNQe155DMbF0RqyndOQ7pOPNdhuocHkOdo1os6nkL6RebnEXHrkEpXDb2o46JTSS0d9iDdRI5Fva7jxjzvpX6NWK4befabpOWADYDngZ82Lo+IK4D7gKWBN3aQ36uAd+a3ZzXJ707gauBVwLsGXfBRpNt13IG/59dKnKOd6mU9S1oJOJrUImEs9+PRizquPR50VOS78LGsR3U8bN+BHfQwM+vcevn12pLl1zak68Rv8utOkt5QXCBpPeADpKbYZw8gz9GsF3X89vz6G0mL5M4gT5b0TUl7SFp4UCUdvXpRxwBI2g3YGvhaRPxzEGWrip7VcTOSJgCL5rcPdCPPEaBWNzdFxDMlaQZSj6sBrwYejYg7upBfFXS7jttZNb9W5RztVE/qWZKA04HxwB5j/Ma8q3UsaWng9aT+Kn4vaW1JUyWdkjvdfNvQizzq9OI8viS/Hirp1bWZ+dw+jNQn1kUR8b+BFrbRmOm0zcxsKPLICIvlt3eXJPtPfl2p03wj4urcwebhwHW5s8L7gWVJnRbeBOyZf8mttF7VMbB2fl0MuA1YsmH5MZJ2johLqLge1nHtV6DjgRtIj22NSb2s4xYOIjUFvi4iZnQpz36r1U1ZHcLA6rGW5j8t0nT7uIx03a7jUvmGZr/8duidEo4uvarnTwKTgc9FxG2DKFeVdLuO18mvM4AvAp8n9Z1S83lJfwDeHxFjpSVuL87jQ0kBkq2BuyX9mdT64w3AiqTWknsPvKhzcksPM7POFEeemFWSpjYs54IDyTgivgLskvN9C7AT8GZSE8LLSKOMjAW9quPaDeiRwJOklh8Lk375PY30C/n5klYfQJ6jVc/OY1JdLkAameGFgRasQnpZx3OQ9FZS0ONl4MCh5jeC1OqxrA5hYPXY7fyqYDjr5NukG6GbSY/AjSVdr2dJryX9T/sbqXPksa7bdVz73rAS8AXgR8AapJGytgBuIX1fO3fAJR29un4e54DRFsAPgQnAu0mdpa5CGqHsioh4alClbeCWHmY2Jkg6Gth2EKtuGRH3MXuEv2skzQ18F9gN+Bbpi+E9pOHQPkkareG9kt4cEff0ogzdMlLrmHqA/2XgHfnZfUgBkD0lvYb0j/azpOMwYo3UOpb0UdKIDUdFxN96sY3hMlLruBlJa5OerR4HHBoR04dr28OgVo/darLf7fyqYFjqRNIXScOyPwHsGBGdPsdfFV2t58JjLa8iBZnHat9JRd0+l2vfG8aTOt7dtbDs95LeDvwL2FzSpNyfRdV1/XqRf2y6iBQk+TBpFKJnSH2HHAOcJmnTiNh9qNty0MPMxoplSL/sD9Tc+bUYaZ6f9OWt0QJN0rZzMLA7cEpEfKow/zZgX0nzAB8Dvkr60jiSjdQ6for0q81lhYBH0XdJQY8tB5Bnv4y4Opa0PHAc6ZydOoiyjTQjro6byV8WLwUWAY6LiK8NNq8RqlY3C7RIM5B67HZ+VdDzOpF0APBl0i/A74yImwaTzyjX7Xrej9TK4MsRccNQClYhvbpeQJOWSRFxr6SLge1J3x3GQtCjq3UsaTzpUbdVgM0i4urC4stzvyk3A7tJ+lFE/H4QZX6FH28xszEhIj4UERrENCOv/yRpGE5Izxk2s3x+nTGAok3Jr3OMJtAw/60DyLMvRnAd19KWPSZUm7/0APLsixFax1uSmvy+CrhE0vTaRH2UkvkK8980wN0eViO0jmcj6XXA5aT+ab4dEQcNJp8RbkZ+LatDGFg91tKs0KX8qmBGfu1WHc9G0r6kgOgzwLsbbmrGkhn5tVv1/N78+rbi9TZfc6fU0uR5vxxgWUerGfm129cLqMB3hy6ZkV+7VccbA2sCdzW7NkTEo8Cv89shfwd2Sw8zs879nXSDtyGps8ZGGxXSdar2BbzZr8EAj+fXxUqWV00v6vhvwPrA4iXLJ+TXmSXLq6YXdQzp2eeyzsvmAiblvyeUpKmSXtUxklYFfg+8htSPyidbrzFq1epmLUnzlYwWsGFD2lZuJd18LybptSUjuAz6uIxS3a7jV0jaB/gm8Cyw7Rhp/l+mV/W8SYtly+Sp7LtF1fTiejGL1FrP3x2Sbtdxu++/0MXvwG7pYWbWuQvz6y6NCySNI3VACnDBAPK8P7+WjWle+1IzVjoz7UUdn59f35QfF2pU+wXhrwPIczTrah1HxLSy1g/UgyCzCvN/PuQ9GPl6cR7XOi/8Pelm5gfAx6s6TGXuw+g6UguiHRqXS5oELAc8CLRtQRARz1P/1bDZcVmZdL19Hrh40AUfRbpdx4X19gJOIo3CsF1EXNqVAo9SPTiXJ7e45h6ek52c5y3SvT0ZuXpQxy8AtVYyczz6mvtje0t+Oya+O/TgelH7/ru6pLLztPbdeOjfgSPCkydPnjx1MJGeVXyA1InTPg3LjsnzrwPUsGxZ0q8GtwLLlqz3P2C9hmUbAA/l5Yf1e/9HcR2LNHZ8ACcD4wvL3kx69jSAbfq9/6O1jltsa2LOb2a/93u01zEpgPSfvO40YK5+7+cw1OP2eX8fAFYpzF+SNJx3AJ9qWOeTuf7OaJLfhqQOjWcBGzUcr+k5v+P7vd+jvI4/luv4WeBd/d6/kTJ1u55bbGdqzuukfu/zaK9j0rCpLwFPkzqqrs0fB3wj53cvMF+/93001jEpeHJfXuc8YKHCsrlIw9kG8ALw2qGW3Y+3mJl1KCJmStqJ9GvhSZJ2A/5N+se4BvAw8MHIV+yCual3jDh3w7KvAJuTAhx/lfQX0ugtK5CaWs9F6iDr6O7v0cjTizqOiJD0QeCPpPHet5Z0HbAU6ZnSccAxEfGLHu3WiNKj89gKelTH55Gel36OdF04PQ3iMIevR8StXdmRPouIn0n6DvAJ4EZJl5K+ANf6kfk5qUVB0QRSHT7YJL9rJX0OOAr4k6TLSc2nJ5G+tP8FOKRHuzMidbOOJa0LnEIKNN8F7ChpxyabfTiq2Q9NqW6fyzanHlwvrpe0P3Ai8FtJ15KCHOsBK5Mey9ghmj/mUUndrOOIeF7SFFLLyPcBk3IdPwOsSwr0vwzsH80fRxwQBz3MzAYgIq6QtB5wGOkivzbwX9IXvcMj4oEB5vekpM1IN+PbA2uRgh1PAVcBPwZOi4gXu7cXI1u36zjnebukdUg3NNsCW5N+vbmc9IvYRd0q/2jQizq22fWgjmvPNM9DGtqvzDTSr2qVEBF7S7oS2IcUnBhH2r/Tge9ExMsDzO9oSTcAB5JafswL3Enqf+LYGHvDqXazjhehPqzl6nlq5m5gTAU9oPvnss2pB9eLb0m6kXS+vpHUP9gDpBFdjozcgfVY0s06jojfSXoDcACwBTA55/cgqSP0EyPiz90ot+b8kcHMzMzMzMzMbPRzR6ZmZmZmZmZmVkkOepiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZmZmZmZmVWSgx5mZmZmZmZmVkkOepiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZmZmZmZmVWSgx5mZmZmZmZmVkkOepiZmZmZmZlZJTnoYWZmZmZmZmaV5KCHmZnZKCZpqqRoMs2UdJ+kv0v6vqSPSlq43+W14SdpWj4npg8hj9p5NaV7JauWqtRR4Zoyo8myyYX9nFiy/sKSjpZ0q6RnCum3K6SZS9I+kv4i6UlJL+c0J/Rsx8xszHLQw8zMrJrmB5YB1gV2B04D7pf0DUnz9bVkZmNMNwJPo4GkccBlwGeA1YB5S5IeB5wEbAQsCGhYCmhmY5KDHmZmZtWxFukGYkFgMeC1wDuAI4H/Aa8GPg1cK2nJfhXSzCrrbcAG+e8vAMtRvyb9AkDSgsA+Oc3PgNcBC+U0Bw9nYc1sbBjf7wKYmZlZ1zwdETML7x8D7gR+K+krpF9WdycFR86TtHlEvNiHctooExH+Jb6NsVBHETGd1q0y1s6vj0fEkSVpVgcf/2hOAAAgAElEQVTmzn9/NSL+3aXimZk15ZYeZmZmY0BEPBMRewDn51lvAj7YxyKZWfW8Or8+0UGadunMzLrCQQ8zM7OxZT+g1rrjgLJEkuaTtL+kP0h6WNLzku6X9FNJk1qsN1vfBZI2kXS+pAckPS3pRkn75Wf/a+ssJ+lESbfnjg/vk/QdSRNa7YikcZJ2l3SppIdyGR+QdIGkd7erCElLSzpJ0gxJz0q6V9JZktbJy2fkfZnaZN3pedm0/P49ki6R9KCkl4odMkqaIGlXST+TdFfe1tOS7pD0A0nrtSjjxEJHkJMlLSDpcEk35zwelvRLSZPb7W8hzw3zcXwgl+UOScdJWrTFOm076ZS0mKQvSro6l+vZXIeX5k4rWx7PJvnN1mmmpCUlHZ/L+6yk/0r6iaR1W+Qxr6StJZ0q6Z9KHfzWzuWLip1rlqw/2zkgaYqkK/L+haT9W9VRTh/ArnnWJM3Z6fDUXM7H8vsvtSnTfJKe6CRtyfqS9DGlTkSfkvR4PmZ7SGrZWqXxmBTmT8/7OTXPWrFhH6fl/QxgeiHLuwppZjTZXu0z/pt8vJ/Pr7+U9N4W5ZytM1ZJb5B0hqT/5Dz+0WSdRfL5e42kRyU9J+nuvF6rc6zxWvA2Sb9WuiY9K+kWSYepg76U8rpnKV0nnsnluF7petjquruq0rXslnyOz5J0k6RjJC3VbrtmlRcRnjx58uTJk6dROpFuMiJPEztc5+LCOks0Wb4m6bGYaDEdXZL3tLx8OulRmhdL1v9hTr8B8N+SNLcAC5VsZ1HgqjZlPBOYu2T9NwAPl6z3DPBuYEZ+P7XJ+tPzsmnAMU3yOKGQ9u9tyvkisFdJOScW0r0PuKkkj5eBgzo4Jh8Cni/J42Zg4ZI8ammmlCx/J+lxqlb7OUc9tjlPJxfW3RK4tyTfF4CdSvI4vk2ZAvgRoJL1a+fA4cC5Tdbdv1UdAVM62P7UnPbb+f0dZeXJ6XYpHPOOPvOFdecGLmxRlrOpX1NmtDkmEwvzp7fZx2nMfq1qNs1o2NaywHVt1jmLJp/x4j4A7weebVjvHw3p3ww81GI7LwH7ltRpbd+nAZ/Lx6VZHlcA40vymB84r925UrLufqTPQNl6jwJvGsh54slT1Sa39DAzMxt7/lT4e+PiAklLA78HVgLuBj5G6hB1MWA94JSc9DOS9m6xjVWB7wCXAJsBE4DXU3+85iOSdiB90X8Y2A5YClgR+HJOszpwSGPG+dfonwKbkr7UfwtYJ29jE+DnOekuwLFN1l8AuAhYHJgFHEgKLiwJbEO66TwD6GSI37cCB5E6ZNw0l2EN0g1yzX2k/lTeRerzYAlgZWBrUv2MA06StH6bbR1HOhaH5NclScGZm0j9LBwj6R0t1l8V+B5wOTApl3VVoNYqZQ3g0HY73EjSZqT6XIQUwPoUaeSOWme6O5GO81D6j/kesACpA8wVgKVJj2fdS+qj7gxJr2+y3kzgJ7kMG5BGNFqWdE5+N5fpQ8C+bba/B7BDXmd9Ut2tx+ytFpo5k9RB51n5/ZXUO/asTUfkZafn15VJN+Flds2vV0TEjDbbb3QEsG3++wLS538CaZ9+RKrTXZuv2tI7SftS68fjP8y+jx/P216Q9DmoKXa+vGZtpqT5gd+R6vgRUgfMq5POqTVzXi8BOwNfa1GuRUnBiFtI+70U6fz5fGFbawG/IdXDjaQ6WIF0fdiEdO7OBXxT0tYttjUpl+vHpFFpFs/7d3Ze/pZcD7PJ17NzSUFNSNfILUnn+JK5DIeRAjiN6+4OnEj6DFxEuh4tldfbFrg+18GFkpZrUXazaut31MWTJ0+ePHnyNPiJwbX02LGwzscalp2d598HLNlmm48Ar25YNq2Q9/k0/GJN+qX5Duq/0N9Bk9YFpJvFAB5ssuz9hW18oclyAedQ/zV8jYblhxTW36rJ+ouRAj6z/RLfkGZ6YfkPh3gMa3V+ZpNlE5n9V9sdS8o7Iy+/qcny4jG5EJirSZqf5eX/LSljbf0pDfPnAm7Ly+4Flm+xn01/5W6RfnJhuy8CmzRJsxKpX4gALh5E3e+Z172n8VzNy2cUynB4m7ya1lHDMZjeJo/rc7rvlyxflnSzH8CuA9zX5ai3vDqvZH9PK+zHjDbHZGKT5VPL1u00j5zmiLz8KWC1kjRTqF9HlispR5CCggu0KE+txdj1wHwlaWrH7+bGemP2a8FJTdYVcG1efk2T5R/q5Bxr/PyQPvdP5fVOLVlnfuqtw74z0M+HJ09VmdzSw8zMbOwpdh64WO2P/Oz3DvntgRHxv5L1v076BX0x0pC4ZQ6KiCjOiIgXqLf2GA98JSKadWZ4Tn5dStIKDct2z693A0c1rpi3+SnSzZAK6Ws+nF9/GxGXNFn/UeArTfdoTi8y9GE2z8yvW7ZJd3VEnNs4M5f38Px2TUkbtsjjwIh4ucn8H+XXJSWt2KYcRe8gDTkK8OmIuKcsYQxtpKBzI+LqJnneRb2lylaD6L+gVvfLUd+PZh6hdYuCbvlBft1B0qubLP8wKdA0kxSoGohdSK2KAjig8bOZHUx6FKRvJI0H9spvvxYRtzVLFxHTgNtJ15EdmqXJDovZR7UqbmsDUgstgI9HxDMledRaQK0BlPXvMYs0TG9jOYP6ebaupLkbktRaGf2T+ud4Dk0+P7uRWj89QXrEpdk6s6i3vtmpXZ8tZlXloIeZmdnYU/ziW7zxeQvpBiKAq5U6zZxjymlqNyIblGzj9oi4s2RZcf7vStLcUfh76VcKnr60b5bfXhgRLzVbOSL+C/whv33lUQFJi5EevQD4Rcm2ITUV78Tf87ZakrSOpJNzp4RPSnq51oEjqY8VgKUlLdgim5+3WHZB4e/NStLcERG3lywrDhs6kMDBFvl1ZkMZuq2TfZ+L9CjAbCQtJelLkq6S9IikFwp1P6uQtFXQ4/KIeH7gxR6wM0l9rixI/XGHoo/k15/mG9qBqN3c/yMi7m6WICIeI/U90U/rkR7JALiy7DqUr0U35HRl16EgPbpSpnb+Pgnc3GI7j5P6/Gi1rT9HxJMly2qfr7kL+4akhYD/y2/PKglItiv7n4DxLcp+S063COlxM7MxZ3y/C2BmZmbDrthXxWOFv2vBANHk+fESS5TMf7DFOsVfU8vSFdMURz1YmHr5b25dNG4itZ4othQptmL4V9mKEfE/SY+TbhRauavNciQdABxN+pW9nYVJTdabubVspYh4XNJ/qfeL0swDLbb7dOHvtqNMFNRuom4eYkuOdkr3nfpNHTTsex7x4gIKN5ottOrDpe1x7oaIeFjSL0iPcO1KvYUAkjYitTSA9LjFQE3Mr63qsra8VQuuXlut8PcfO1yn7Dr0UFkrj4ZtLUTnw+eWbWswn6+J1H+Evr7D7dfUyv5Oyq8ZjZYgtY4xG1Pc0sPMzGzsWbXwd/GLeicddzaap2R+0xYYjcpaajQotkxZoPB3q5sZqN8IFFtPzF/4u90v5e3yh9mDM3OQ9CZSB6TjSKO47Erq0HUJ6h04FjtHbPWDVKflXaBkeUfHhNnru51a3XZ60zVYpfseEc9R37dX9l3SIqS+KxYlBdcOInUw+RrSub4g6Wa3plXdtzzOXVbr0HQLScsX5u+aX++k82BAUe3c78Z530vdvA61O27Dfs1j9s9X8do00M9QN8tuVmlu6WFmZjb2FB8B+HPh79rN0BMR0a6FQ78Ub8jKbu4blxdvJoo3fMUASKv1h6LWN8GdwKYRMUd/CZJe1WFenZZ3OG9amwWWeqF03yXNQ70VTXHftyeNoPESsHlEzNHCIQdGRprfkDoSXpbUh8cR+RzZKS+fVtIfRzu1c384zvuhKH5GF42Ix4dhW9dHRFlfHb1UvDYN9DM0izTizIkRsX/3imRWPW7pYWZmNoZIWgZ4e37794h4uLC41tfGwpJWGt6SdewJ0vP1UG/qX2at/Frsv6D4d2kfDpKWoP2jLZ1YJ79e1CzgkTUbarWZ1csW5Jv3Wl8cTftr6JFaU/k1cweUvVK678x+HhT3vVb3NzYLeGSd1v2wya2fzshva607tiF1HByFZQM1I7+2qstOlvdasc+f9YZpW6tJGshjXd1yF/UWIuu0SthErey9riOzUc9BDzMzs7HlBOotPY9rWHY5aYhXSMNBjjj5F+6r8tv3SGraT4akJUkdswJcWVj/UeqdsL67xaa2HWJRa2rNycvKKeq/4LezXYfLripN1X2X5dcFaF2+oepk318GiiO8tKz7bOehFGoAXsivnfTrAvVHXF4naRPqwY/fl3VC2oE/5dd1y0bokbQoMGmQ+XfLn6m3gJjS423VOlKel84/h10TEU+RhrMF2GWAo6vUyr6ZpFW6WzKzanHQw8zMbAyQNI+kU6gP7fgH6sPCAhAR9wI/zW8PlrQpLUhaMT9aMNxqN4QTSf00NHM88CrSL+OnNyyrdQ75Dklva1wx3/gd2jh/kGodYL695DGWg6m3SGlnE0k7Ns7MI9J8Kb+9OSKubUzTQ7+jHkT6hqRlyxIOsSXIjvnmvzHPlYBa0/5LGkbSqdX96s1uCiVtBnxsCGUaiEfy62s6SZxH2an12/EZUmeVMLgOTGvOIrUqEHBcyQ32UaQAQN/kUXK+nd9+WNL2rdJLWjJ/ZgezrauBv+S3R0tqNYIPklZrtXyQTsqva9PiutPk83Ma6RGXccAP80gtpdrtm1mVOehhZmZWHa8uDFW4iKSJkt4q6aukpu175nQ3AtuXdCK6P3A/6cbncknHSNpI0oQ8vV7SFEk/Jz3a0Ou+HJq5gHoLgyMlnSBpLUmL5bKeR/0X/G9FxC0N658I3Jv/Pl/S/pJWyPv3LlJAaGHqj9EMRS2ItBpwoaSN83bWkXQy8HVmH32klRnAGZI+L2mlhvJOzGkO6EKZO5aH2NwDeBFYHvirpH0lrVo4B98n6SfA54awqXuAX0n6hKTl8jC0HyANr7owqSXFZxvWOZ/U+mNu4GJJ75a0dK67g4BLmH2o3l66Lr++VtLHJS0haXyeyr6P14J17yW1znqK1DHroOSg5vH57fuB8yRtmD8360o6gxQEmjHYbXTRV0jXKQE/kXSqpLfkAMdiklaXtJOks0jlHcpQrHuQ+oKZAFwj6YuS3pC3s6Sk9SXtJelS6q0yuuls6sNWf1nSuZI2z+fIhHyMPk/DaFP50cS989tNgeskfVTSKvmzt4ykN0s6RNI/gW/0oOxmo0NEePLkyZMnT55G6QRMJbVm6GSaSXqkZd42ea4K/LOD/F4kdTRYXHdaXja9Rf5Tanm0SDOxsJ3JTZYvRnqMo1X5zgLmLsl/PdKv783We5bUh8Ld+f2hTdafnpdNa1OX40g312VlvIo0ekvt/cQW9fA+0jC9zfJ5GTiopAydHJN29V1bNqVk/a2BJ9scj6kDPLcnF9bdktS5Z7N8XwB2Ksnj0BbluR9Ys9W+kW6oOyp7m3zmI7U86bheSB2OFuv0e124XswNXNiiTs6hfk2Z0eaYTGyyvHTdTvMopFuKFNBrdx16GXjDQMvRkH4jUmCt3bYeGcy1oIN6m7/NcQlKrpfA7qRRatqV/byhnj+ePI3WyS09zMzMqukZ0jCd1wM/IP2Cu0xEHBjlHWoCEBH/BtYl9SPwC9Kwts+TggEz8ryPAUtHxGO92oE2ZXyU1GfHHqS+SB4h3fw+SLp52CYidomIF0rW/zupE8tvA/8h7d8DwE+AN0bEL2g++stAy/kSKYDyeVLA4jlSZ6x/BQ4k3Qy1G0K05lFgY+AI0iMlz+Z5vwK2iIhjB1vOoYqIi0m/tn+VNDTvE7l8d5Eegdkb+NYQNnEHsEHO4y5SPf6P1JJmw4g4p9lKEfFV0iNdV5Lq+WlS3R0HrBcRNw+hTB2LiGeAN5MeSbiTVP5268wCzi3MmtaFcrxA6gNlT+AaUp08mf/eC/jgULfRLZEeVZpEaunyU1JQ4jnSZ/U+4LfAp4AVIuL6IW7rGlLHxvsCl5LOrRdI58vtpOOwM/UWVV0VEbMi4j2ka8V5pP17nnRdu4F0nXpLybqnkz57XyNdVx4jPcb0ZF73u8DbgA/0ouxmo4Eiot9lMDMzMxtRch8Bj+a320fEoB8rGGI5JlLvm2LziJjej3L0g6TJwO/z25UiYkb/StMfkk4C9gFuj4hV+10eM7PRyC09zMzMzOZUHNnlutJUZj2SOwmutbyY1seimJmNag56mJmZ2ZiTRzwpWzYB+HJ++9eIuKssrVkP7Uzqu+ZF0iNqZmY2CA56mJmZ2Vi0n6QrJH2kMNrBSpJ2J/VvMDGn+1L/imhjjaRxkuaVNAk4Ms/+cUTc389ymZmNZkMZL93MzMxsNHsLJZ0DkkY7+FxE/GoYy2N2B7Bi4f1jwBf6VBYzs0pw0MPMzMzGoh+RHhvYElgJWAIQaRjTPwDfyiO8mPXDo8DVwGcj4t5+F8bMbDTz6C1mZmZmZmZmVknu08PMzMzMzMzMKslBDzMzMzMzMzOrJAc9zMzMzMzMzKySHPQwMzMbgSTNJWkfSX+R9KSklyWFpBMGmM/8kvaW9FtJD0p6TtJjkm6VdKmkL0vaUtK8TdadmrcZkiZ2sK1ptfQdlm1SIf9nJC3UwTqTC+sUp5fzfv1N0tclLd9JGUYSSRML+zO53+UZbpK2knSJpIckvZTr4R/9LpeZmY1uHr3FzMxsZDoO2H8oGUhaB7gAWLlh0auARYDVSKOXfBHYDZg2lO0NwocKf88LbA+cPsi8RNqn9fO0j6RdIuKioRXRhoOk7UjnqpmZWVe5pYeZmdkII2lBYJ/89mfA64CFgAWBgzvMYzHgd6SAx/PAacAkYEVgArAO8FHgIuCFLha/I5LmIQU5ij48wGz2ItXJgqT6WQP4GvASsABwjqRVhlhUGx6fy683ARsBi5KO6yZ9K5GZmVWCW3qYmZmNPKsDc+e/vxoR/x5EHvsDS+a/d4yICxuWPwLcCHxf0jLA/IMq6eBtQ2qZAXAxsDUwSdLyEXFPh3k8FxEzC+9vBQ6V9CzwFWA+4ABg7y6V2Xpn7fx6WkRc29eSmJlZpbilh5mZ2cjz6sLfTwwyj7fm1383CXjMJiLuH2RgZShqrTr+QwpKBOkRlV26kPexwDP57y27kJ/1Xu2cH+z5bmZm1pSDHmZmZj0iaRVJ35b0L0lPS3pK0g2SjpA0oUn6qbkT0OmF2XcVOrecMYDN1/J/cvB70BuSFgfemd+eHRH/Af6Q33+o+Vqdi4hngTvz2+U6LNM4SQ/kej62Tdq5JN2f036jYdmykvaS9EtJ9+SOY2dKukXSyUN53KZwHkxpkWZqJ+eKpPdL+nnej+ckPSLpcklTJJV+P5S0sqRvSbpZ0ixJz0q6N3cge6KkzQewP690SluY/YOGDmonNqwzr6RPS7pK0qO57PdIOlvSZi22NaW4LUkr5c/mHXkfHu+wzEflfJq2Ril25itpjybLF5D0Ql6+dZPl4yTtrtTJ8EOSns/n5QWS3t2iXMUOfidKWjSXtXbtuUfS6cUOfvO29lTqLPlxpQ6TL5M0qYN6WETSFyVdUzgOd0s6Q9K6Ldabnss4Lb9/m6Rf5319Nn9ODpM0X7syNMl7n5z380qP97VKu3ch7eINyyZJOkfSjFymmZLukvQHSV+StPpAy9ZB2WvnzfT8fhNJ5+dj/7SkGyXtJ2lcYZ3l8mfudqWOoO+T9B01+d/SZHsbSPp+Pv+fzsf+ulz3TTuUVkNHy5LmkfRZpf9ps/I5dJmkdzZb36yvIsKTJ0+ePHny1OWJ1JLheVILhmbTo8CbGtaZ2iJ9ADMGsP0/53WeBl4zyH0olmdiB+mn1dK3Sbd3Id+18ryPFuat12LdyYV0U1qku762/wPY3xPyOvcCc7VI99ZCGTZoWPZYm2P4NLBtSb4TC+kmN1neyX7XjlnTcwVYGPhtmzJeBizYZN0tc/lbrfuPAdT35DZ5zXbekfqjubVN+qNKtjWlkGYz4PGG9R7vsMzvzOlfBBZqsnxGIc8fNVm+VV72ErBww7JFgava7N+ZwNxt6vItwF0l698DrEDqOPiXJWleAN7Vog7eDDzUoowvAfuWrDs9p5lG6sfl5ZI8rgDGd3ou5bwn5LIH8PE2aa/M6X7RMP8LHZyTJwykXB2WfVrOezqwez6/mm37hzn9BsB/S9Lc0uzczOsJOKpFvQdwN7BGm+vTNsBfWuSxZ7fryJOnoUxu6WFmZtZl+ZfSaaR+OW4HdgCWJt1s7EUKeCwKXCxpxcKqR5A6b3xXYd5a1DvrXHMAxbgsv84H/EppONCR0pdX7dGWf0TETfnvnwLPNSwfFElzA7UWFfcPYNWz8uuypE5fy9QewbktIv7WsOx20k3F20jHawKpI9odSIGo+YAzJXXUAqWblFpw/DyX7WngS6S+NBYj1ddn8vwtgO83WfcHpPLfTqqDVYDFSfu5FXAS8L8BFOmP1M/tmmLntAuSbsBQGlL5V6QRh54Hvpz/XiKX98q8/sGSPt1muz8hfQZ3Jh3rZYCPdFjmK0k39eNIN/+vyJ/lFal3DDy5yfq18+ofEfHKozySRPoMbEq6afwWqbPhCaTOXH+ek+5CenyrlR+Srj0fJu3bMsAnc7mWI52fR5KCWF8AVs3b2Yb0eRkPnJo/R7ORtBbwm5z+RuCDpOva4rmc55Fakn+zWUuWhno4AvgxqePaxUnXurPz8rcAH2+zn7OJiIdJAT1Ix7apfJw2zW/PKsxfndQXEKROoN9O2rclgfWAHUnH6Bl6Z1XgO8AlpODcBOD1wPl5+Uck7UCq54eB7YClSOfdl3Oa1YFDSvI/nNQZtkj/o2rbWIZUZ3eR9vmXSh1qlzkxb+fTpM6yJ5D+b9Va2B0vacmSdc2GX7+jLp48efLkyVPVJuAG6r+qLtlk+brAsznNOU2WT6bJL90DLMMSefvFX9+eBC4Fvk66wZnj1/yGPKYW1l2TNCJKq+nMWvoWea5SyPPAhmU/y/MfAMaVrF+smyklaQ4opPneAOvttrzeaSXL56XeSuCLA8x7HOkxniB1UNu4fGKh3JObLG+53w3HbEaTZXtSb6UwqWT9zan/CrxxYf46he2v04PPTLtjemAhzc5Nlr+KFEQJUuBmsYblUwrr/xdYeghlvSbnc0zD/F3z/J8DD+a/X9uQ5uo8/7iG+e8vlO8LTbYp4Jz/b+/Og+WoqjCAfx8xsioBFSMGQRCQAiSoKGJEFFxKQcAEFHDBfd8pUVER9w1BXBEUBFQWAdFSC1F5BBGKqIiYiJoocSkDoijIGpLPP8696X6d7p55b6ZfHlPfr2qqZ6bv9OuZ6Z569/S556b1q1G5El85L25Bze8GolMsRNBmFYADatqUs5ieXbM+Z6JcC2DDhs/n9NRmCQBW1o2Vtv/5hve5KK2/ehLfzeGlz2hOQ5t3pza3Adio9Pyb0/MrUJNN0+Wt9JkJEeCofm4zASxDkYmzDJVModQu/wavqFn36PS9C8AxDfsxu3TsHl1Zt01pH1cC2LPm9buW2rx+Kj9D33xruznTw8zMbIhI7oFiJorjJK115VvSrwGcnB7OJzmr2mZQkv4JYB4iyJE9AHF192jEVLU3kjyF5Ow+NrkY0Ulou/VThDTX7FiNuMpbdlZazkZRiLUvDFuSfCfiKjYQ/5ifOJHtoLjSPJ/k/WvW748YIlJu2xdJqxAdV2DdFFh9U1qeKumyugaSLgXw0/SwfLV8Run+RLJnhuXlaXmFpLU+d0n3IDqtQGSjHNayrU9KWjHAvuTPbp/K8zmLYwxFjZo1bUhujBiWUN5Glt/fckQmxjiSBOAtiGOapfZ1TpJ0Q83z56TlegAul/S9mjY/QWQQAJGBsQbJx6HIkHiNpKaMh/em5U6IAG+d2xFZJuOk95l/B+bWZZv08J20baL5GMjH9YWS7ig9n4/xmyVN+TTeJUelz2GNtD852+N+AD6kUqZQSf59eSjJR1TWvQHx3V+PyLJZSzovPp8eNmbLIIL1V9W8/joAv04P92h5vdmUctDDzMxsuOaV7l/Q2CrSpIH4B3bPLnZE0nJJz0B0tD6BGIN9d6nJhohaGteQnMjQmUHkoMelkqqd5x8grlKX27VZU/QSEUT5O+J93h8xBOKVkn47wf3L6e6bYfwwoyx3BK6StKxuAySfTPI0ktcziteuLu3nF1KzHSa4XwNJqea7pIeXMwpq1t4QwxaAooMORAbMXen+aSS3m6JdRypKmY/P85vaSboGcQUcqAw9qfjhgLs0lpa7V4o+7lNaP1Z5DoihBDMRx2oOiuShLbkI60UpOLYWSTeWXtf2/n7U8PyfSvcvafgbKrWrBkOfnpa3AljScvz8B1HzAxh/DJVdJampyHKeSWom4jzsWwpilIcCjUPyMSjOg29UVufO+s4kP0JyQn97SJZK+lPDup7fH4rjH2j+/i4DsHHL97cktdulIfALxBCnJvn7e2hLG7Mp5aCHmZnZcG2dlisk/bul3eLS/eoVuaGS9CtJ75K0JyLb4wmIVPfcMZkN4OzU+WrySElsuyFqCTQi+SQAubN8VnV9ulqfg0EHpyvjE7EKUW/iZABzJZ0xwddD0lLE8AWgcqWT5KYoAiHVDlNu8xlE3YcjETUnNkFcda7atOa5LpWDLGehPWPnrandQ/ILUmcyX8HfH8BSkr9Ns0W8kJUZMIasfH4saWwV8nnVdk79ebDdWbuuB2NWlEcigna/QX3QI9//jaTybDGbojgehvH+arNYKpkZbZkuuV11BpUd0/KBiKmF246hfOw8BPX+0fL3y9kXE57FBcW5uVtNMDcHQm7C+Cy4nOWUs1/eA+AmxixBH0s1kdafxL5MVD/fS1u7cpvqZ5d/A16D9u8uBxbXQ9RaqdPP9zeZ786sEw56mJmZDdcmafm/Hu1uK91vKxg3VJJWSlok6VjE1fN8VW5XtF89HoZcoHQVgGUk51ZvAKKiSrQAAAlKSURBVK5JbTYG8Pwe2ysXvdxY0v0kbS/ptZJ+N8B+5k7T/pVifgsArI+oiXFu9UUkX4Qo7AcAlyIKH+6EKPKX9/N1af2M6us7Npkgy7hOnqTjEQVZf5Ge2hnxHXwLwD9IntXnUKmJ2qR0v9/zqvGcahmW0Zc0rCBnBexTWS6UtFrSEkTHek4pK6Y8/KVsqO8PcX710k+barBu4GNogn+/bh/6cQmKgO6awGUK6r4wPTy7IaNmAaLmx3JEFt5eiFlmfghgBcnjWrIfhqGvz6UpG6hizWeXAsiTKWQ9yPc3me/OrBMOepiZmQ1X7rRs0tpq/PrbGlt1SDHbQXlc/e5d/a00Nv/Q9DAX9Lym5val0st6DXG5W9L/0u2OHm0n4hzEP/UbAji49Hy+SnxJXa0WRAAAiEyA/SSdJ+l6Sf/K+4kohNqlpo7N7aX7u/fK2km3baobkfRtSXsAeBiig/hZRNr9TMTnc0WPWR8moxwI6Pe86vqcqtb1yMuxUps1dT1IboSixkG1nsd0fH918jF0bZ/HDyV9YKp3UtK9KOqXlOt6PAVFhkxtppakeyR9PB37OyFqp5yBmO1nFoD3oyZL7T7gTsSwKgB42wS+vxvW4T6bDY2DHmZmZsN1Q1rO7jEmfOfS/eXd7U5P5WE2G3X4d56D5lTpJvuSfFgXO9Mm1U7Iqe+HAwDJLVFcqa/tMCFmOAGAb0ta3dBml4bn+5FrarSljTd9XuV6AAMHtyStkHS+pLciZuR5e1q1LfqrxzIRf0HMBgFER7RNPq+6PqfG0jLX9ajL4sj390FkDMxEvI+FGO+/iDoYwPR5f3XyMbQjyek+dCGfo9uSzDWTctbHUklX17xmnBSwPE3SSxFT/ebsrkNI9vqeppX0e5SPmc6C22bTlYMeZmZmw/Wz0v224RkL0vJeRIHRdWVO6X6Xs3LkoS3/BbBBj9oguUMxA+2zcHQpd5r2S0VAD0P831QulFiVU8Frh66kq/0HDbBPeRz/9g3bXw8Ns8JI+guAP6SHRw6wD3XblqQTEN8tEFNjDnP7/0ZR62J+UzuSuyECMMD487ALlyOunM9ADJnYDkU9j2wsLZ+KIhPkumqtn1Q89Ir08ECSTcfPFgD2Tg+7fn91cvHMDVAME5mW0swiuajnESnT7JD0uClo2ba9O1HMCgUM+RifIvn7O7CLGcPMpjMHPczMzIZI0i9QzH5xLMkHV9ukGQRybYfzK0UNh4LkR9P0uW1t1kdRnHI1iqlKh70vsxDFLwHgAkl3t7WXdD2KmgkvbmvboQsRAY4ZAF6A4irxdyTd3vCaXCBz/4b1n8bEs13KFqXl/Iaiim9BUUi3zglpuTfJt7e0A8kHlLNsSD68rbBs6pDnYS3/atv2JH0tLeeRfEHN358J4KT08A6sPR3yUKVz9tr0MA8RW1jJ8FmCqC2xFYrsl7GGTeb3tw2AoxranICYmUil9lNG0pUoArSfJNk6AxHJHdvWT4E8tfGhiHNy88rz45DcPgUOm5RnLOriGO/aSYjf+U0BnNo2HTDJGVM5Q5NZ1xz0MDMzG743If653ApR4+BgkluQnEPyVYjgwvqIqR/f1dE+PBPA1SQXkTyK5F4ktyQ5i+SjSL4E0YHJV45PlvTXjvblEBRZEP12RnO7uSQHGRIyKan+xnfTw3cAeGy633aVOM888zSSZ6birA8i+QSS5yACXYMUWM2z4zwCwEUkdye5GcldSZ4A4HiMH8ZSdQqKYTvHkzyP5DNJzk7beVQ6Vr8C4K8oplEFgGcA+BvJk0keRHK7dCxtTXJ+2u56iMylxmllB/BFFEOxziD5/rS/DyL5VMRV7Hwsv7fHzEnDkmtz5EDTWHllyuBYWGlTreeRXQjgJ+n+x0ieSHJnkpun4+d8FIG3zw1YqHcQr0DUIHkw4vflfSR3S/u5BcnHknwtyR+jCNKtK/lc3QJFwG+RpD80tD8GwB9Jfpjkvun3ejOSO5B8A+L8AWK41ZXlF5I8ncW01NOSpMUAjksP5wO4kuRh6RyeRXKr9L4/gpgF622NGzO7j5lMFV8zMzNrIekyki8DcCpimsALaprdAuB5HRaKuzUtH59ubb6OyBLoSr7KfRP6zyY5G8DHETMAvAjdBYfafAORxp87rDcD+FFL+08AOADAboh9rta2uADA9wF8dTI7I+n7JL+J6Pw+K93KvpD28diG168ieTCA0xGdngUohlnVuafyeBaAV6dbnXsBvD51roZK0l0knwvgYsTUqceh6MCVfSoNtZkKYyim982P69rkITl19TxihSSShyKmTN0LcT7WnZPfRHMmSOckLSa5LyKwNQcx9fUHG5pPReCpkaTfk/wlgMehOId7DW3ZFhH8OKZh/c0ADpG0cjh7OeU+hAjIfwDxudRmvSStGXlm9yXO9DAzM+uApDMQRQe/jLhqdidi9oPrEGPDd5DU5bj8/QDMQ3QML0YUsbsL0TG9BcCvEJ3kJ0o6sqt/4klujWIq3PP6nGox16D4eXp4RI+0865cjPFp7OemmSFqpeyQpyCCNcsArER0/H6GuEK+AMUMCpP1EgBvRgz/uRNRR2MhgEMlvbHXi9MsMgsAPB3AmYjMkDvSvt6IyER4N+L4/G7ppeciAjonAbgawN/Sa25HDOP4IoDHSDoFHZG0HMBcRNHUKxHFP+9J+3I2gHmS3tnV369xOYoCq9V6HtlY6f7iNGNSrZSdsjfiWPkp4thbiajlchGAAyQdsa473KkI6A6IjLYfI4KZKxHH0VLEsXI4YqjOulYOcqxCMatLnaMR59eZiKFLNyF+L/+DyIo7FsCj+ymCOl2l+jsfRtRNOhFxzN6K+GxuAfBLAJ9BZHmts+Ca2bAxMu/MzMzMzMzMzEaLMz3MzMzMzMzMbCQ56GFmZmZmZmZmI8lBDzMzMzMzMzMbSQ56mJmZmZmZmdlIctDDzMzMzMzMzEaSgx5mZmZmZmZmNpIc9DAzMzMzMzOzkeSgh5mZmZmZmZmNJAc9zMzMzMzMzGwkOehhZmZmZmZmZiPJQQ8zMzMzMzMzG0kOepiZmZmZmZnZSHLQw8zMzMzMzMxGkoMeZmZmZmZmZjaSHPQwMzMzMzMzs5HkoIeZmZmZmZmZjSQHPczMzMzMzMxsJDnoYWZmZmZmZmYjyUEPMzMzMzMzMxtJ/wfNiBszEppmwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 460.8x338.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 318,
       "width": 542
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.group_difference_plot(shap_values_F, sex_F, X_F.columns, xmin=xmin, xmax=xmax, xlabel=slabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identifying multiple potentially offsetting bias effects can be important since while on average there is no disparate impact on men or women, there is disparate impact on individuals. For example, in this simulation women who have not shopped at brand X will receive a lower credit score than they should have because of the bias present in job history reporting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How introducing a protected feature can help distinguish between label bias and feature bias\n",
    "\n",
    "In scenario F we were able to pick apart two distict forms of bias, one coming from job history under-reporting and one coming from default rate under-reporting. However, the bias from default rate under-reporting was not attributed to the default rate label, but rather to the brand X purchase score feature that happened to be correlated with sex. This still leaves us with some uncertainty about the true sources of demographic parity differences, since any difference attributed to an input feature could be due to an issue with that feature, or due to an issue with the training labels.\n",
    "\n",
    "It turns out that in this case we can help disentangle label bias from feature bias by introducing sex as a variable directly into the model. The goal of introducing sex as an input feature is to cause the label bias to fall entirely on the sex feature, leaving the feature biases untouched. So we can then distinguish between label biases and feature biases by comparing the results of scenario F above to our new scenario G below. This of course creates an even stronger demographic parity difference than we had before, but that is fine since our goal here is not bias mitigation, but rather bias understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 97%|=================== | 9720/10000 [00:11<00:00]       "
     ]
    },
    {
     "data": {
      "image/png": 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ASsBJIYSv1J3XEJS1y/iKNANpw07PbyToeptI+hTwReIRyz1DCLfXmc8w1+l2Pox4VPyLIYS/DaZgI0i3thdQcCYthPCApN8C7yLuO4yGYKKjbSxpLPGSxw2A14cQrstNviLdl3IH8H5JPw0hXFmjzP/WkcucQgj/FUJQjaE/5X+a2N0lxOu4iqydxv0DKNq0NF6kd5Gmz98wgHn2xBBu4yxt2eVi2eerDWCePTFE23g34qnfJYGLFZ/c3iepj0aPRcvkPt9xgNVe7IZoOy9E0quBK4j3AH0/hHBEnfkMYf1pXNZ+MLA2zNKs06H5jQT9adypNl6IpEOJBxrmAns37SyMJv1p3Kl2fkca757f3qZt7rQsTfrsogGWdbjqT+NOby9gBOw7dEh/GneqjbcFNgFmFm0bQghzgN+nt4PeBx4SN2AnNxN3nLYm3mTabJtcunZlf2xFRy4hPswOGtfvjXTdaOMbga2Iz5soskoaP1syfaTpRhtDvLa07KarJYh9UEOjvUe6brUzkjYkPqRudeK9Kh+vzjEsZe2yqaRlSnoP2bopbZU7iTu1EyStX9KjU+3vZJjqdBv/m6RDgO8ALwBvHSWXgZTpVjtvXzFtjTSU7VuMNN3YXjxHPLPsfYeo023cav8XOrgPPFRuwAY4P40PaJ4gaQzxxmmIjxZv16w0LuuTN9tYjJabsLvRxuel8Y7psrFmWcR7wwDmOZx1tI1DCDPKjtTTCC6ey33+m0HXYHjoxrqc3Xh5JXFH4cfAh0dil5DpHrGbiGe83t08XdIU4vMKHiY+H6LV/F6kcZSr6Dt5FXF7+yLxaawjXqfbOJfvI8RnHswD3h5CuKwjBR6murAuT63Y5h6Xkn0vfbZS52oydHWhjecD2VmdRS6BTve77pzejop9hy5sL7L9340lla2n2b7x4PeBwxB46l/6rx5P42l9hzRNOzF9fhNNT0okdvN6ZxrWLMn3KLBl07TXAY9R8sTWkTh0qY1F7Ps4AN8Dxuam7US8ti8Ab+l1/YdrG1csaxKj9wnY3ViX1yN2vReAGXTgqcRDeSBej5w9UXWD3OerErvNDsAnmvJ8PLXdGQXz25rYEcNzwDZN31Vfmt/Jva73MG/jD6U2foEOPd19JAydbueK5Uxn9D4Bu9Pr8hbEB68+T+xcI/t8DPDNNL8HgGV6Xffh2MbEoOTBlOdcYIXctCWI3cYG4rM+1h902XvdeE2Vn0Lj8d43AD8n3iASiDv+GxXkmZSmB2BS07QVaDye/CXgT8A5xKjuJRqPcl+613Ufrm2cpm9AY8eun3i24lpgQfrshF7Xe7i3cclysjyjLpjoRjsTg49A3FE7gxhQFA0b97ruHWzD76c6zwUuTL/dp9JnvwbGNKWfnm03S+Z3ZJq+ALiU2LXjI+mzPwPL9rrOw7WNgcnEQCIQu84sWz+/0es6D+d2brGMLM+oCya60cbAoWmdfiltH34F3J3yPAls3+s6D+c2Bnan8R85m3j2+Dxit7DZfvEhHSl3rxuuoPIbEW+Mfph4Gvc+Yo9Aq5ekn0T1ju5SwCeJO7dPEv/kngCuBj5K7kj6aBk63cYpzUTgW2klnZfa+FLi9bw9r/NIaOOKPKMymOh0OxMD4dDGMLXX9e5wG+6fto9PE88q3AgcQsGZmao/rlyaNwF/SNuAucQjav8NLNXrug7nNiY+QK2d9bO/1/Udzu3cYv5ZnlEZTHSjjdN6fRFxZ/dF4kPbTmv3f3AkDp1sY2BD4FRij5BzUxvfRzz4tl2nyqy0MDMzMzMzswEZSjdgm5mZmZnZMOJgwszMzMzManEwYWZmZmZmtTiYMDMzMzOzWhxMmJmZmZlZLQ4mzMzMzMysFgcTZmZmZmZWi4MJMzMzMzOrxcGEmZmZmZnV4mDCzMzMzMxqcTBhZmZmZma1OJgwqyBpuqRQMDwr6UFJN0v6X0kflLRir8tri5+kGWmd6BvEPLL1alrnSjayjJQ2ym1T+gumTc3Vc1JJ/hUlnSDpTklzc+nfnkuzhKRDJP1F0tOSXk5pvtW1ipnZqOVgwqye5YA1gMnAQcAPgVmSvilpmZ6WzGyU6URANxxIGgNcDnwG2AhYuiTpScB3gW2A5QEtlgKa2ajkYMKsfZsS/5iXByYA6wN7AF8DHgWWBT4J/FXSqr0qpJmNWLsDr0uvjwbWorFNuhBA0vLAISnNr4BXAyukNEcuzsKa2egwttcFMBtGng8hPJt7/wRwD3CppC8RjwQeRAw6zpW0SwhhQQ/KacNMCMFHjlsYDW0UQuij+izCZmn8ZAjhayVpNgbGpddfDiH8s0PFMzMr5DMTZh0QQpgbQvgAcF76aEfgP3tYJDMbeZZN46faSNMqnZlZRziYMOusw4DsbMSnyhJJWkbS4ZKuljRb0ouSZkn6paQpFfkWujZc0vaSzpP0kKTnJd0m6bB0bXWWZy1J35b0r3TD5oOSTpW0SlVFJI2RdJCkyyQ9lsr4kKRfS9q7VUNIWk3SdyX1S3pB0gOSzpS0eZren+oyvSBvX5o2I71/m6SLJT0s6aX8jaSSVpF0oKRfSZqZlvW8pLsl/VjSlhVlnJS7gXWqpPGSjpN0R5rHbEkXSZraqr65eW6dvseHUlnulnSSpJUr8rS8uVjSBEmfl3RdKtcLqQ0vSzfbVn6fBfNb6GZfSatKOjmV9wVJj0g6R9LkinksLWkvSadL+rtixwTZunxB/qbgkvwLrQOSpkm6KtUvSDq8qo1S+gAcmD6aokU7S5ieyvlEev+FFmVaRtJT7aQtyS9JH1K8+fkZSU+m7+wDkirPrjR/J7nP+1I9p6eP1m2q44xUzwD05WY5M5emv2B52W/8kvR9v5jGF0l6R0U5F7qJXNIWks6QdF+axy0FeVZK6+/1kuZImifp3pSvah1r3hbsLun3itukFyT9Q9KxauNetZT3TMXtxNxUjlsVt4dV290NFbdl/0jr+HOSbpd0oqRXtlqu2YgXQvDgwUPJQPzzDmmY1Gae3+byTCyYvgnx8qhQMZxQMu8ZaXof8ZKqBSX5f5LSvw54pCTNP4AVSpazMnBtizL+DBhXkn8LYHZJvrnA3kB/ej+9IH9fmjYDOLFgHt/Kpb25RTkXAB8pKeekXLp3AreXzONl4Ig2vpP/Al4smccdwIol88jSTCuZvifxsrqqei7Sji3W06m5vLsBD5TMdz6wX8k8Tm5RpgD8FFBJ/mwdOA74RUHew6vaCJjWxvKnp7TfT+/vLitPSndA7jtv6zefyzsOOL+iLGfR2Kb0t/hOJuU+72tRxxksvK0qGvqblrUmcFOLPGdS8BvP1wHYB3ihKd8tTel3Ah6rWM5LwKElbZrVfQbwufS9FM3jKmBsyTyWA85tta6U5D2M+BsoyzcH2HEg64kHDyNt6HkBPHgYygP1gon/zuXZu2naajR27vuBDwKvIu68TwZ+kMv7sYJ5z0jTHgTmARcBOwCvIN2rkcv/7rSM24G3AasC6xB33LI0xxcsQ8BlNHaovkO8VvsVwHbAr3P5v12Qfzxwb5r+LPEMzbrARGIQ8ff0B5ztHE8vmEe2A5Ht4P4S2D6VYWNgh1zai4BTiDvcrwVWAdYD3gz8PuVfAGxVsJxJubrMJO4UHZ2+k4nAXqm8WZo9WnwnLwAXAzunsm7AwjvcJ5asM4vsKOemvZ7GzszDxJ2bV6d15lXAe4g32h4zwHV7alPdnwQ+BqwNvBLYD7g/TX8ReG3BPL4EnJ3KsBWwOrGXsx2AU3PlPqykDP1N3/OpwJap7SYDk6vaiHjf33hiYBuAa9L7/LBkSvsfuXnsXNEul6Y0V9bYXuQD3/OIvSm9ItXpjFxbL7JzX/CdTMp9vkyqy1fTtHub6rgUsGR6vWduHpvk0iybm99yxOA2EIP+w4m9Q60MvAb4Co0DFYsc2KCxXXwKeIYY0L+FuI1ZG9gzl3ZT4PmU/m9pvVqb2InFdsR1NyvvXhXbgpnE7dGZwNYp/ybpfZb/kJLtWf4Az7nArsR1fGIqw+eBmQV5D8rlO58YdK+a8r0FuCVNexxYa6DriwcPI2XoeQE8eBjKA/WCiX1zeT7UNO0sGjueq7ZY5uP5HYA0bQYL76yoafo44pHXQNyRu5uCo+E0dr4eLpi2T24ZRxdMF3EHMgs2XtM0PR9Mvakg/wQawUarYCKQzrIM4jvM2vxnBdMm5ZYTgH1Lytufpt9eMD3/nZwPLFGQJttheqSkjIXBBPFS1Lto7HCvXVHPwqOyFemn5pa7ANi+IM16xB3GAPy2RtsfnPLe37yupun9uTIc12JeVQFX9h30tZjHrSnd/5ZMX5N4lDwABw6wrmvR2AE/t6S+P8zVo7/FdzKpYPr0srztziOlyYKSZ4CNStJMo7EdWaukHIF4sGJ8RXmyM5y3AsuUpMm+vzua242FtwXfLcgr4K9p+vUF0/+rnXWs+fdD/N0/k/KdXpJnORpnM08d6O/Dg4eRMvieCbPOy9/0OCF7ka6tfXd6++kQwqMl+b9OPKI/gdj1bJkjQggh/0EIYT6Nm8DHAl8KIRTdhHl2Gr9S0jpN0w5K43uB45szpmV+griToVz6zHvT+NIQwsUF+ecQj2i3YwGD787yZ2m8W4t014UQftH8YSrvcentJpK2rpjHp0MILxd8/tM0XlXSui3KkbcH8SwEwCdDCPeXJQyD6znsFyGE6wrmORPI7k95U43rw7O2X4tGPYo8Tjwa3m0/TuN3S1q2YPp7iQHcs8QAcCAOAMYQdyw/1fzbTI4knr3qGUljgY+kt18JIdxVlC6EMAP4F3E78u6iNMmxYeFe7vLLeh3xLBXAh0MIc0vmcUwav4Z4RqrIc8Szhs3lDDTWs8mSxjUlOTSN/07jd7yIgt/P+4lndJ4ing0syvMcsWtwgP1a3RNjNlI5mDDrvPwfSn6HYmfiH3MArlO82XeRIaXJ/uBfR7F/hRDuKZmW//wPJWnuzr1e7d8Fj3+Gr09vzw8hvFSUOYTwCHB1ertTLv8E4uUSkPq9L3FBxbS8m9OyKknaXNL30s2U+Sf+BuIlDgCrKfbBX+Y3FdN+nXv9+pI0d4cQ/lUyLd8950B2yHdN42ebytBp7dR9CeKlZguR9EpJX5B0raTHJc3Ptf1zuaRVwcQVIYQXB17sAfsZ8ZKt5Yn3yDR7Xxr/Mu0oDkS203xLCOHeogQhhCeI1/b30pbEy5kA/li2HUrbor+ldGXboQBcUrGsbP19GrijYjlPEu+pqFrWn0MIT5dMy35f43J1Q9IKxMvbAM4sCfRblf1PwNiKsv8jpVuJ+Owhs1HHz5kw67wVc6+fyL3OdrJFvLyjHRNLPn+4Ik/+6F9ZunyafC8oK9Io/x3VReN24tH+/JmN/FH3/yvLGEJ4VNKTxD/gKjNbTEfSp4ATiEeFW1mReOlCkTvLMoUQnpT0CDEQKDuz8FDFcp/PvR7IE9KznZM7BnnmoZXSutPYWYKmuqcecH5NbgeuwooV01p+z50QQpgt6ULipXwH0jiijaRtiEfGIV52M1CT0riqLbPpVWccu22j3Otr2sxTth16rOysRNOyVqD9bmrLllXn9zWJxkHTW9tcfiYr+56UbzOaTSSezTEbVXxmwqzzNsy9zv8BVu1MlVmq5PPCMwbNys4sNMmfSRmfe121kwCNP9j80f7lcq9bHdltNX9YOOhZhKQdgZOIgcTNxB3E1xL/1LMnA++Vy1J1AKXd8o4vmd7Wd0L1Q8maZW3b7s5MXaV1DyHMo1G3f9dd0krEewNWJgatRxBvOF6duK4vT9yJzFS1feX33GE/SuNdJa2d+/zANL6H9ney87J1vxPrfTd1cjvU6ntb7Ns8Fv595bdNA/0NdbLsZiOaz0yYdV7+UpA/515nOxlPhRBaHZHvlfyOTtlOc/P0/J90fkcqH1hU5R+M7Nrve4g9PC1yPbqkJducV7vlXZw7g0UBWzeU1l3SUjTO+uTr/i5iT0UvAbuEEBY5Ip8CjqHmEmIHCGsS75H4alpH9kvTZ5Tc79BKtu4vjvV+MPK/0ZVDCE8uhmXdGkIofZZEF+W3TQP9DT1H7Bnu2yGEw1slNhvNfGbCrIMkrQG8Mb29OYQwOzc5u5dhRUnrLd6StbRA1IUAABreSURBVO0p4vXL0Ljko8ymaZy/Pjz/uvQaeUkTaX2JUzs2T+MLigKJ5LVtzmvjsglppzi716HwevguyS6Z2CTdONstpXVn4fUgX/es7W8rCiSSdtt+sUln685Ib7OzEW8hdngQctMGqj+Nq9qynendlr+nqvSBjh1e1kbtPFSuC2bSOKOxeVXCAlnZu91GZsOegwmzzvoWjTN+JzVNu4LYlSrEbheHnHRE9tr09m3KPUk7T9KqxBvKAf6Yyz+Hxs3jVU/Jfusgi5rJLisoK6doHHFupeppzflp15am6rzL03g81eUbrHbq/jKQ7/Gpsu2T/QdTqAGYn8bt3DcDjUudXi1pexpBxZVlN0+34U9pPLmsxy7Fp6BPqTn/TvkzjSP207q8rKwDiKVp/3fYMSGEZ4jdxgIcMMDelrKyv17SBp0tmdnI4mDCrAMkLSXpNBpdKF5No/tVAEIIDxAfvgZwpKQdqCBp3XSJyeKW7WhNIl4HX+Rk4kOyQi59JrupdQ9JuzdnTDtUxzR/XlN24+4bSy5nOpLGGZRWtpe0b/OHqYeqL6S3d4QQ/tqcpov+QCM4+6akNcsSDvLMxb5pp7p5nusRH2gGcHFTz1pZ229ctLMl6fXAhwZRpoF4PI1Xbydx6nUruy/iM8SbbKHejdeZM4lHwQWcVLLjejxxx7pnUq9Z309v3yvpXVXpJa2afrN1lnUd8Jf09gRJVT16IWmjquk1fTeNN6Niu1Pw+/kh8VKnMcBPUs9NpVrVzWwkczBh1r5lc10CriRpkqQ3SPoy8RKHg1O624B3ldz8fDgwi7hDcYWkEyVtI2mVNLxW0jRJvyFe4tLta+WL/JrGEfGvSfqWpE0lTUhlPZfGEedTQgj/aMr/beID1gDOk3S4pHVS/d5MDLRWpHE51WBkwdlGwPmStk3L2VzS94jP7GguX5l+4AxJR0lar6m8k1KaT3WgzG1LXVl+gPi8jbWBGyQdKmnD3Dr4TknnAJ8bxKLuB34n6aOS1krdvb6H2I3pisQj/59tynMe8WzFOOC3kvaWtFpquyOITwL/J4vHTWm8vqQPS5ooaWwayv7nsiD4HcSzic8QbyivJR0sODm93Qc4V9LW6XczWdIZxOCqv+4yOuhLxO2UgHMknS5p5xQ4TJC0saT9JJ1JLO9gujz9APFem1WA6yV9XtIWaTmrStpK0kckXUbjLEInnUWje+gvSvqFpF3SOrJK+o6Ooqn3uXSJ6sfS2x2AmyR9UNIG6be3hqSdJP23pL8D3+xC2c2Gh14/Nc+Dh6E8sPCTXlsNzxIvbVq6xTw3JD5AqdX8FhBvkMznnUGLJ/3SeHJtqEgzKbecqQXTJ9B4cm3ZcCYwrmT+WxKPFhfle4F4jXr2FOxjCvL3pWkzWrTlGOJOa1kZryX25lT4ROCmdngnsTvcovm8THxIYFEZ2vlOWrV3Nm1aSf69iH31V30f0we4bk/N5d2NeFNy0XznA/uVzOOYivLMAjapqhuNJ2C3LHuL+SxDPFPSdrsQb5TOt+n/dGB7MY74FPSyNjmbiqdYs5iegJ3SvZIYKLfaDr0MbDHQcjSl34YYsLZa1uN1tgVttNtyLb6XQMn2kvhQzrltlP3cwa4/HjwM18FnJszqmUvsDvNW4lN1PwSsEUL4dCi/ERiAEMI/iU95PZD4YLeHiA/SeoG4c3Vhmt9qIT7karEL8d6HnYlHFa8gBgbziXU+H3hLCOGAEJ+4XZT/ZuLNt98H7iPW7yHgHGC7EMKFFPcGNdByvkQMTI4iBgLziDeR3wB8mriT0e7Dx+YA2wJfJV5a9EL67HfAriGEb9Qt52CFEH5LPDr8ZWIXuE+l8s0kXgr1MeCUQSzibuLDwk5J85wHPEo887N1COHsokwhhC8TL+37I7Gdnye23UnAliGEVs8q6YgQn6y8E/HSlHuI5W+V5zkg/8TzGR0ox3ziPSYHA9cT2+Tp9PojwH8OdhmdEuIla1OIZ2Z+SdzZn0f8rT4IXEp80v06IYSBPqOheVnXEztkOBS4jLhuzSeuL/8ifg/70zgD2FEhhOdCCG8jbivOJdbvReJ27W/E7dTOJXl/RPztfYW4XXmCeDnb0ynvD4Ddgfd0o+xmw4FCCL0ug5mNMuka7Dnp7btCCLUvLxlkOSbRuPZ/lxBCXy/K0QuSpgJXprfrhRD6e1ea3pD0XeAQ4hPlN2yV3szMFuUzE2bWC/menm4qTWXWJalzg+xMwYweFsXMbFhzMGFmHZd6QCqbtgrwxfT2hhDCzLK0Zl20P/HeoAXESxXNzKwGBxNm1g2HSbpK0vtyvZ+sJ+kg4vXjk1K6L/SuiDbaSBojaWlJU4CvpY9/HkKY1ctymZkNZ918oqqZjW47U3JTI7H3k8+FEH63GMtjdjewbu79E8DRPSqLmdmI4GDCzLrhp8TLR3YD1gMmEvu0n0XsjvKU1OOTWS/MIT7N+7MhPh/CzMxqcm9OZmZmZmZWi++ZMDMzMzOzWhxMmJmZmZlZLQ4mzMzMzMysFgcTNmJJWkLSIZL+IulpSS9LCpK+1euyLW6Spqe693dh3lPTvEN6orQNI5L2T934PpH7jfym1+UyM7PhwcGEjWQnAd8FtgGWJ/YmZLaQ4RYM5co6rQPz+gRwJrEL35Xwb8TMzAbIXcPaiCRpeeCQ9PZXxL7kHyY+3+DFXpXLbIj5XBpfBXwcuA94mditr5mZWUsOJmyk2hgYl15/OYTwz14WxmyokTQRWC29PTmE8PdelsfMzIYnX+ZkI9WyuddP9awUZkOXfyNmZjZoDiZsSJO0gaTvS/o/Sc9LekbS3yR9VdIqBemnSwpAX+7jmbnrzPsHsOyFrqWXtLKk43NluV/SjyStncszRtLB6abvJ9ON35dLmtLG8raSNENSv6QXUv7rJR0laXyLvJL0obTcZ1Le6yR9QFLb18FL2kfSbyTNkjRP0uOSrpA0TVJXtxd169/OPQRlN6CndeXK3Ef5dWWh9GkdyD6fKmm8pOMk3ZHWh9mSLpI0taIcfSn/jIo0hfdwZHlzSX/cVNa27vlI32UA8m1xZX4+BXlWlHSspBslPSVprqR7JP2vpE0rlrVQu0vaQtIZku6T9KKkW1qVN+U7J83nlyXT+3Ll361g+sa56YuUV9LSkj4p6VpJc9K6f7+ksyS9vqJc0/JtJmltSd+TNDO10UxJJ0uakMuzjKTPSLol/VafkHSBpM3baIc1FLdBt+a+h39JOlXSqyry9adyTk/v91XjpvvnJd0s6TBJY1qVoWDeJ6Z5P9wqv6QTUtpHJY1tmvZWSedLejCtG0+nuv1B0pHKbWc7pfn3KGlPSZdIeix9N3+V9N6mPBsrbvez7dRMSV+XtFwby9strVP3qbGNu07S4ZKWKsnT/D+0gqSvSLorff+PS7pQ0nYdaRSzOkIIHjwMyQF4L/H+hlAyzAF2bMozvSJ9APoHsPypuXw7AzNL5nk/sA6wNHBRSZr5wJsrlnUU8Vr1snLfC2xckncccH5F3rNy7VJYf2BF4NIWbXc5sHyLdppU87seTP2zNNMq5l9Y/xb1XSg9MCn3+TuB20vyvAwcUVKOvpRmRpvr3aSCvFVDy/YHprWaT1P6ycBDFekXAIe2andgH+CFpry3tLl+fDSlfxRQ07SlgLm5eX6pIP9H0rTHCvKvC9zZok2Ob6MttwJml+S/lXiD+yuAv5SkeQbYqqIN3gU8V1HGucA+JXn7U5rpwKkV8zizxm93ci7/HhXpRLwnJwCnNE07rdU6CRxeZ9vSoux9ad4zgGMrln1cSv8m4NmSNFcDY0uWsyTw0xb1uxVYvcX24PXAP0vyv0jFf4wHD90cel4ADx6KBmAK8FLaSP4z/ZG+Elgb+DDweJr2FLBuLt+SwHhgz9xGdpP02Xhg2QGUIb8Rnwk8APwXsHoaDqER7PwcODn9oR8FbEDccdgbeDCleQAYV7Cc9+WWcwOwBzAReBXwWeD5NO1eYMWC/Cfm8p9H7L3qFcCWwBm58gcKggniGcor0/Tn0p/qa4GVgfWBI2jsxPyiRTtNqvFdD7b+Wd5pFcuYXlT/FuvKQusLCwcTM4k7xkenck4E9gL+nkuzyI4VgwsmlkllyqZ9uKms42naUS6Z/9iUdpPcvPbMzyeXdiKNjgueBg5P7TCRuG7flpvHOyra/SnizvLNwFuAVYm/5T3bXEfyZd20YFuR7UwF4JqC/D9P085t+nxpGkHhPOA44NXAKsAuwDW55X6yYL7TmtaJO4C3p/qtw8IHN75G/H3OAT6Wpk8EDkjtE4C/lNT/DTS2h1enNlyd+Dvflcbvdx6wZUH+/jT9HmKw+21gc2ACMQj6Q66ce9X4DWdt+JOKNFNyy9gu9/nuuc9/DuwIrEHc3v8H8aDS74BDBlquNsrdl/vuAvDj1B4T0rKvTp+/RNwuPQlcC+yW1pENgB/kyv/RkuX8JE2fD3wTeF1axjrAwcQgNwB/AsZUbA/uAWYBBwFrpfXnPbn8D1LwH+PBQ7eHnhfAg4eiAfhb2jjeD6xaMH0yjaOcZxdMz2+AJ9UsQ34eTxTNB/hi7s/mJeAtBWnekJvPm5qmLZX7I7iZgmAHeHMu/9ebpq1FPDIcgHMp2JkEfpjL318w/eA0bQEwpaQtdqFx5mDbTrX1YOufptcOJgZSfhYOJgKwb0GaCTR23G4vmN5HzWBiIPVts+3z9ZlakuaU3Pq9U8H0lYC7aATLY5umT88t43ZygUqN8j6S5nNI0+dfSJ//b1qH5wHLNKWZldIc2vT5p3Pl279gmUvSCCieByY0TZ+Wy38PsHLBPLKAfj4x4HldQZoP5ubzmqZpY4mBdAAuBpYoyD+GeOYwAL8vmN6fm/8iZ82Iger9lBwwaOO7OZpGwLl0SZrs7MPdTZ9/M31+42DW55rrVF+uXU4uWb+zQG8+cWd/yYJ02Try54Jpb8wt44CScmxC46DJe5qmTc3lfwpYvyD/3rk0PjvhYbEPvmfChhxJWwObpbfHhRAebU4TQriF+OcEsI+klbpcrO+EEPoLPj8njZcgHhG9sCDN5cTLHyCeNch7K/EIF8BnQgjPN2cOIfwOuCC9PUha6B6IA4g7EgH4VAghFCz/SGLgVebQNP6fEMJVRQlCCFcCV6S3+1fMa6AGW/9euS6E8IvmD0MIc4hHtwE2SevysJSuaX9fevvzEMI1zWlCCE/S6F52TeLR2zLHhhCeHUSRrk7jqU2fT0njC4GbiAHADtlESa8mHsWH2AVu3kFpfG0I4azmBYYQXgQOS2+XAf6zonxfCiE8UfB5to0YC5wVQrixJE32221eZ95GPIIdgINCCC8XlPMlYlAFsEf+Ho0m9xJ33pvzzyV2oV20/Haclcq3PPGsyUIkjSOeXYb4XJO87D6Lh2ost1OyM7ILSev3pentWODotE40y77jLZvvBaGxfb0khNBc92w5dxDbEKq3r98JIdxd8PnviGe8oN73ZzYoDiZsKNox9/q8inTZzZhjgW7ffHZpyef35F7/oShB2sHP0q3WNDmr65PEoKNMVteJwEa5z7OdpltCCPeWLP8JFt2JAkDSqsRLmgCuUbypuHAgXtIC8RR9pwy2/r1S9YToX+del968OwxsBqyQXp9bke4i4uV9ADuVpAnAJYMsT18aZ8EDkpYk/vYDMdjI0kzN5ctez6GxDpN2uDdJb0vrF0K4Gch24MrqB4PbRjxDPEMHi24jdk3jfwBPV/w+70rpRLxUp8hlRcFIknWf/cqS6aXSgZY/pbcHFCTZk3jWDho7zZnsJvw9003gLW9k7oLr0ndQJPv+XgD+WJImWz+WpFFP0g3p2fp6VYvta9Y1c9X2tfA3lL7TrAwD/v7MBsvBhA1F66bxw+lIb5nbc6/X6WJ5IF43voh0RK8yTZKlW6bp86yud5acVciU1XVSlr8ib9X0V+de/4x4XXvZcHhKN7HFsgZisPXvldL2TkczH0lv1y1LNwzky35HWaIQwnwaO6Jl381jgzwrAY2AeKIaPTJtR/xN/S1tK/rS51Nz+bLXVzetY/myltYvyda/qnWv7Pc/2G1EFjxvQvXvM38Gt+w3WnX0Pzsr2Lz8dmVH3fcsOFOcBRg3hhCafzs/I55RWoJ4L8dsxR7wviBpSp0epmpo53uZHUIoe5hj/jvOt9/qxLM1AF+l+vs7OaWr2r528/szq83BhA1FWTegrXY+8keSli9N1RkvdShN8yU6g61rdhTvuRb5y+a/Yot8RQq7MKxpKH7X7Wi3vSu79B3i8mVv9/sp+27mlnw+ELfTuFxwahpnR3370viPxN/hNpKWKUmT6WT9skuNWqmzjejkb7Sd5df1C+J9BUvSuKQJSflLnxa5zCcFo7sAXycGREsTz8ZMJ35nD6QzFt28vLFT3x0s/P3V+e6WHGQZhsJloDbKOJiwoajdHbH89LJT1EPdYOua7dS2ujSgbP75neItQwhqY5jUYlkDsbi+6+brmAer3fZu3kmtOvuS6XRZ68qXvd3vp2u/w3RWofm+iWzcl9I8TbyRf0lgB0kbEnsGgkUv9RtS9auQ/UbPb/P3qRDCjMVdyBDC4zQuw8lf9/8O4tHyl4GzS/I+HUI4iniJ12Rib1e/JNZ9NeIZi+O7U/Kuym9f39Hu99ez0prV5GDChqL+NF5N0soV6fIPnyq8X2AY6E/jjVsceSur67/zt1hO2fT89dxbtphHN/Sncd36Q+Pm8qrT+6tXTKujtL3TJR7ZdctDoax19edev6YsUbrhNLtcrtu/w740npIe8rU9jfslmtNMpXFW4gliD3F52TMPoKJ+Sbb+9WI7k/1Ge/H7HKjszMMUSWum11lgcUUIofIm6xDdGkI4NYSwL7H74Oy7PVzSChXZh6JZNH7zw+H7M6vFwYQNRfmb3N5ZkS47lb6A+CCo4Sir60o0brQsktX1MeD/cp9nNz1OllR4fX4KyKYUTQsh3Jeb37Q2yttpg60/NK533rAoo+KTuxd5KnLO/Nzrdq/Pfnub065tmlZZ1mT3FsvOrtvu9rXktxG7ooT4wLkye9EIjspuUO2Uf983QVxf8/dLZPrSeCqNMxfXNN94nPJk90qU1k/SFsTnCUD361cku2l7HUm79GD5A3EB8YzPEsB+qYOHN6RphT0ZVUmdR2T3EowjPvdm2Eg9P2XB0AEFPT2ZjQgOJmzICSHcQKPXlS9IWqU5jaTNiU/FhfggqicXV/k67EIavbickLvO+98k7UFjB/VHTTeRnkm8jlbASSVH948nXodcJvuz3lnSp6oKK2l5SZ08cj7Y+gP8NY33SUerm32C6huhH8+9brdu20vat6CsE2h00XlHCOGvTUmy95NzNxHn828D7Ndi2Vl5u3oGI90DcEZ6+5+SdmhOk44UZ5efPMDge2xq5TYa9T8qjfua0lxDum+CRhDZnCbzozTeUdJ7miemLk2/k94+T3yo2uJ2LvEZEACnSarsrUdSz3o7S107Z72Z7U98oNoY4tH5wp752ihvPoB4vDTV0JVtX9enfBsNgKSlyg4KmQ1lDiZsqDqUeI3t2sC1kt4haVVJa0n6EPGZB0sRH5L0uYr5DGkhhHnEB2dB7M6xT9IbJa0iaT1Jn6HxJ3wf8Sm6+fwP0Piz2gc4V9LWkiZImizpDOBDLHzJSrMfApel1ydJ+mUqw2qSVpa0QWr/04k7NR3r7nSw9U9+ksbrAOdL2jKVezNJJwMnsfDlXM3+RVyPAD6b6ruUpLEVPcn0A2dIOiqVcxVJbyYehZyU0hQFZr8k7pQK+I2kPdJ3NUnSYcTuRe8vyJd3UxofKGlbSculsnbjqOeXiD1TjQF+n26EXVfSxFTfa2j0NnRYRW83HZECyex5F9lOV19TmqeJ3Y0uSaOb1cKukYHv0+ip6QxJx6bv/xWSphDPCuycph/Tone5rkhHt6cRA6QNgZslHS7pNZJWSr/TbSV9UtKfaTwvoleyMxBb0fhtX5i+lyKnSbpN0tGSdpK0evpNbCLpKOArKd116Uzqv0nqkxQk9Xe8Fh0SQriY+GRtiM8suUzS2yStmb6/SZLenLZV9wLv7llhzeoKQ+DJeR48FA3EB2a9yMJPHc4Pc4AdS/JOzaWbVHP5bc0jl2ZaRZo+Kp58TDzK+nJFXe8FNi7JOw44vyLv2VQ8ATrNYzxxJ6RsHvnhrV1o69r1T/nPrMj73Tbq//WSvP25NJNyn7+TeIlMUZ6XKXjKcG4+B5fkC8B1LPw020Xak9hnf1n+ttufNp6AndJNJnZJWbbMBTQ9WTqXt7Lda64rn2hq6wkFab6RS/MkBU+NzqVdl9jVb9U6f0JJ3mlZmg60c39KM71k+puJ9360+n0u8iTpVvNuty5tfj9jiJfz5cv0tor0fW3U6W6Kn/yc5a21ftHeE+lbrsO0fmr9OOB7bdQzNP+WWs17IHXx4KFbg89M2JAVQjiDeOPjD4hHj+cSe8e4jXiE+tUhhF5cw9xxIYSvEZ9cegZxx3ke8Wj5DcDRwKZh0f7Zs7zziZcBHQxcT2yjp9Prj1D91N5sHs+GEN5FvG/hp8Qj+c8T7yd4hHhk9yhim19QOqOaBlP/5H3Eo363ENeTp4hnCfYNIXy8jSIcTXyOxl+JPfaEFunnANsS+46/i3gZxxzik2h3DSF8oyxjCOF04o7hFamcc4kPrPoc8d6Wym5KQwi/JwYclxK7Su1md5+E+LT5jYk7VTcT22ceMJN4mdDkEMIp3SxDk/xZhub7JTJ9udeL3C+RF+LDHicTzyRdRww+XiRetnU28YDFkYMt9GCF+CT49Yi/wz8SL/l5ibi+/IP423k7PX5QYoiXx52T++gJ4PcVWQ4EPkw8a3c78Xe0gFi/q4lnNzYLxU9+HhZCCPNDCIcQH0h3GnGb8SyxnrOJ692XiT3qLc7fkllHKIRW/5lmZiZpEnEHGmCXEEJfzwpjZmY2RPjMhJmZmZmZ1eJgwszMzMzManEwYWZmZmZmtTiYMDMzMzOzWhxMmJmZmZlZLe7NyczMzMzMavGZCTMzMzMzq8XBhJmZmZmZ1eJgwszMzMzManEwYWZmZmZmtTiYMDMzMzOzWhxMmJmZmZlZLQ4mzMzMzMysFgcTZmZmZmZWi4MJMzMzMzOrxcGEmZmZmZnV4mDCzMzMzMxqcTBhZmZmZma1OJgwMzMzM7Na/h+LPjxW1ZI+ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 460.8x79.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 123,
       "width": 393
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_values_G, sex_G, X_G, ev_G = run_credit_experiment(\n",
    "    N,\n",
    "    default_rate_sex_impact=-0.1,\n",
    "    include_brandx_purchase_score=True,\n",
    "    job_history_sex_impact=2,\n",
    "    include_sex=True,\n",
    ")\n",
    "model_outputs_G = ev_G + shap_values_G.sum(1)\n",
    "shap.group_difference_plot(shap_values_G.sum(1), sex_G, xmin=xmin, xmax=xmax, xlabel=glabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SHAP explanation for scenario G shows that all of the demographic parity difference that used to be attached to the brand X purchase score feature in scenario F has now moved to the sex feature, while none of the demographic parity difference attached to the job history feature in scenario F has moved. This can be interpreted to mean that all of the disparity attributed to brand X purchase score in scenario F was due to label bias, while all of the disparity attributed to job history in scenario F was due to feature bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.8x403.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 367,
       "width": 542
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.group_difference_plot(shap_values_G, sex_G, X_G.columns, xmin=xmin, xmax=xmax, xlabel=slabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "<!--## Things you can't learn from a SHAP fairness explanation-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fairness is a complex topic where clean mathematical answers almost always come with caveats and depend on ethical value judgements. This means that it is particularly important to not just use fairness metrics as black-boxes, but rather seek to understand how these metrics are computed and what aspects of your model and training data are impacting any disparities you observe. Decomposing quantitative fairness metrics using SHAP can reduce their opacity when the metrics are driven by measurement biases effecting only a few features. I hope you find the fairness explanations we demonstrated here help you better wrestle with the underlying value judgements inherent in fairness evaluation, and so help reduce the risk of unintended consequences that comes when we use fairness metrics in real world contexts."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
